# mealpy **Repository Path**: frontxiang/mealpy ## Basic Information - **Project Name**: mealpy - **Description**: No description available - **Primary Language**: Python - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-10-05 - **Last Updated**: 2025-10-05 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

MEALPY

--- [![GitHub release](https://img.shields.io/badge/release-3.0.3-yellow.svg)](https://github.com/thieu1995/mealpy/releases) [![Wheel](https://img.shields.io/pypi/wheel/gensim.svg)](https://pypi.python.org/pypi/mealpy) [![PyPI version](https://badge.fury.io/py/mealpy.svg)](https://badge.fury.io/py/mealpy) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mealpy.svg) [![Downloads](https://static.pepy.tech/badge/mealpy)](https://pepy.tech/project/mealpy) [![Tests & Publishes to PyPI](https://github.com/thieu1995/mealpy/actions/workflows/publish-package.yaml/badge.svg)](https://github.com/thieu1995/mealpy/actions/workflows/publish-package.yaml) ![GitHub Release Date](https://img.shields.io/github/release-date/thieu1995/mealpy.svg) [![Chat](https://img.shields.io/badge/Chat-on%20Telegram-blue)](https://t.me/+fRVCJGuGJg1mNDg1) [![Documentation Status](https://readthedocs.org/projects/mealpy/badge/?version=latest)](https://mealpy.readthedocs.io/en/latest/?badge=latest) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3711948.svg)](https://doi.org/10.1016/j.sysarc.2023.102871) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) --- MEALPY is the world's largest Python library, offering a comprehensive collection of cutting-edge meta-heuristic algorithms. These include nature-inspired algorithms, bio-inspired algorithms, black-box optimization, global search optimizers, iterative learning algorithms, continuous optimization, derivative-free optimization, gradient-free optimization, zeroth-order optimization, stochastic search optimization, and random search optimization. All these methods fall under the category of population-based metaheuristics (PBMs), which are among the most popular algorithms in the field of approximate optimization. For detailed updates in each new version, please refer to the [ChangeLog](/ChangeLog.md) file. * **Free software:** MIT license * **Total algorithms**: 233 (206 official (original, hybrid, variants), 27 developed) * **Documentation:** https://mealpy.readthedocs.io/en/latest/ * **Python versions:** >=3.8x * **Dependencies:** numpy, scipy, pandas, matplotlib, tqdm ## 📌 Goals Our goals are to implement all classical as well as the state-of-the-art nature-inspired algorithms, create a simple interface that helps researchers access optimization algorithms as quickly as possible, and share knowledge of the optimization field with everyone without a fee. What you can do with mealpy: - Analyse parameters of meta-heuristic algorithms. - Perform Qualitative and Quantitative Analysis of algorithms. - Analyse rate of convergence of algorithms. - Test and Analyse the scalability and the robustness of algorithms. - Save results in various formats (csv, json, pickle, png, pdf, jpeg) - Export and import models can also be done with Mealpy. - **Solve any optimization problem** ## 📄 Citation Request Please include these citations if you plan to use this library: ```bibtex @article{van2023mealpy, title={MEALPY: An open-source library for latest meta-heuristic algorithms in Python}, author={Van Thieu, Nguyen and Mirjalili, Seyedali}, journal={Journal of Systems Architecture}, year={2023}, publisher={Elsevier}, doi={10.1016/j.sysarc.2023.102871} } @article{van2023groundwater, title={Groundwater level modeling using Augmented Artificial Ecosystem Optimization}, author={Van Thieu, Nguyen and Barma, Surajit Deb and Van Lam, To and Kisi, Ozgur and Mahesha, Amai}, journal={Journal of Hydrology}, volume={617}, pages={129034}, year={2023}, publisher={Elsevier}, doi={https://doi.org/10.1016/j.jhydrol.2022.129034} } @article{ahmed2021comprehensive, title={A comprehensive comparison of recent developed meta-heuristic algorithms for streamflow time series forecasting problem}, author={Ahmed, Ali Najah and Van Lam, To and Hung, Nguyen Duy and Van Thieu, Nguyen and Kisi, Ozgur and El-Shafie, Ahmed}, journal={Applied Soft Computing}, volume={105}, pages={107282}, year={2021}, publisher={Elsevier}, doi={10.1016/j.asoc.2021.107282} } ``` # ⚙️ Usage

🛠️ Installation

* Install the stable (latest) version from [PyPI release](https://pypi.python.org/pypi/mealpy): ```bash $ pip install mealpy --upgrade ``` * Install the alpha/beta version from PyPi ```bash $ pip install mealpy==2.5.4a6 ``` * Install the pre-release version directly from the source code: ```bash $ git clone https://github.com/thieu1995/mealpy.git $ cd mealpy $ python setup.py install ``` * In case, you want to install the development version from Github: ```bash $ pip install git+https://github.com/thieu1995/mealpy ``` After installation, check the version to ensure successful installation: ```bash $ python >>> import mealpy >>> mealpy.__version__ >>> print(mealpy.get_all_optimizers()) >>> model = mealpy.get_optimizer_by_name("OriginalWOA")(epoch=100, pop_size=50) ```

## 💬 Decision Variables Before we dive into some examples, let's briefly consider the type of problem you're aiming to solve with MEALPY. Understanding your specific problem and its desired solution can help you select the most appropriate approach. To assist you in choosing the right tools, refer to the table below. It outlines different types of **decision variables** available in MEALPY, along with their syntax and common problem applications. This will guide you in defining your search space effectively.

| Class | Syntax | Problem Types | |-------------------|-----------------------------------------------------------------------------------------------------------------|-----------------------------| | FloatVar | `FloatVar(lb=(-10., )*7, ub=(10., )*7, name="delta")` | Continuous Problem | | IntegerVar | `IntegerVar(lb=(-10., )*7, ub=(10., )*7, name="delta")` | LP, IP, NLP, QP, MIP | | StringVar | `StringVar(valid_sets=(("auto", "backward", "forward"), ("leaf", "branch", "root")), name="delta")` | ML, AI-optimize | | BinaryVar | `BinaryVar(n_vars=11, name="delta")` | Networks | | BoolVar | `BoolVar(n_vars=11, name="delta")` | ML, AI-optimize | | PermutationVar | `PermutationVar(valid_set=(-10, -4, 10, 6, -2), name="delta")` | Combinatorial Optimization | | CategoricalVar | `CategoricalVar(valid_sets=(("auto", 2, 3, "backward", True), (0, "tournament", "round-robin")), name="delta")` | MIP, MILP | | SequenceVar | `SequenceVar(valid_sets=((1, ), {2, 3}, [3, 5, 1]), return_type=list, name='delta')` | Hyper-parameter tuning | | TransferBoolVar | `TransferBoolVar(n_vars=11, name="delta", tf_func="sstf_02")` | ML, AI-optimize, Feature | | TransferBinaryVar | `TransferBinaryVar(n_vars=11, name="delta", tf_func="vstf_04")` | Networks, Feature Selection |

## 📚 Optimizer Classification Table * Meta-heuristic Categories: ([Based on this article](https://doi.org/10.1016/j.procs.2020.09.075)) + Evolutionary-based: Algorithms inspired by Darwin's law of natural selection and evolutionary computing principles + Swarm-based: Algorithms drawing inspiration from the collective movement and interaction of swarms (e.g., birds, social insects). + Physics-based: Algorithms derived from physical laws and phenomena (e.g., Newton's law of universal gravitation, black holes, multiverse theory). + Human-based: Algorithms inspired by human interactions and behaviors (e.g., queuing search, teaching-learning processes). + Biology-based: Algorithms based on biological creatures or microorganisms. + System-based: Algorithms inspired by ecological systems, immune systems, or network systems. + Math-based: Algorithms developed from mathematical forms or laws (e.g., sine-cosine functions). + Music-based: Algorithms drawing inspiration from musical instruments or compositions. ![MEALPY3-0-0](.github/img/mealpy-classification.png) * Difficulty - Difficulty Level (Personal Opinion): **Objective observation from author**. Depend on the number of parameters, number of equations, the original ideas, time spend for coding, source lines of code (SLOC). + Easy: A few paras, few equations, SLOC very short + Medium: more equations than Easy level, SLOC longer than Easy level + Hard: Lots of equations, SLOC longer than Medium level, the paper hard to read. + Hard* - Very hard: Lots of equations, SLOC too long, the paper is very hard to read. ** For newbie, we recommend to read the paper of algorithms which difficulty is "easy" or "medium" difficulty level. [//]: # (

Group	Name	Module	Class	Year	Paras	Difficulty
Evolutionary	Evolutionary Programming	EP	OriginalEP	1964	3	easy
Evolutionary	*	*	LevyEP	*	3	easy
Evolutionary	Evolution Strategies	ES	OriginalES	1971	3	easy
Evolutionary	*	*	LevyES	*	3	easy
Evolutionary	*	*	CMA_ES	2003	2	hard
Evolutionary	*	*	Simple_CMA_ES	2023	2	medium
Evolutionary	Memetic Algorithm	MA	OriginalMA	1989	7	easy
Evolutionary	Genetic Algorithm	GA	BaseGA	1992	4	easy
Evolutionary	*	*	SingleGA	*	7	easy
Evolutionary	*	*	MultiGA	*	7	easy
Evolutionary	*	*	EliteSingleGA	*	10	easy
Evolutionary	*	*	EliteMultiGA	*	10	easy
Evolutionary	Differential Evolution	DE	BaseDE	1997	5	easy
Evolutionary	*	*	JADE	2009	6	medium
Evolutionary	*	*	SADE	2005	2	medium
Evolutionary	*	*	SAP_DE	2006	3	medium
Evolutionary	Success-History Adaptation Differential Evolution	SHADE	OriginalSHADE	2013	4	medium
Evolutionary	*	*	L_SHADE	2014	4	medium
Evolutionary	Flower Pollination Algorithm	FPA	OriginalFPA	2014	4	medium
Evolutionary	Coral Reefs Optimization	CRO	OriginalCRO	2014	11	medium
Evolutionary	*	*	OCRO	2019	12	medium
***	***	***	***	***	***	***
Swarm	Particle Swarm Optimization	PSO	OriginalPSO	1995	6	easy
Swarm	*	*	PPSO	2019	2	medium
Swarm	*	*	HPSO_TVAC	2017	4	medium
Swarm	*	*	C_PSO	2015	6	medium
Swarm	*	*	CL_PSO	2006	6	medium
Swarm	Bacterial Foraging Optimization	BFO	OriginalBFO	2002	10	hard
Swarm	*	*	ABFO	2019	8	medium
Swarm	Bees Algorithm	BeesA	OriginalBeesA	2005	8	medium
Swarm	*	*	ProbBeesA	2015	5	medium
Swarm	*	*	CleverBookBeesA	2006	8	medium
Swarm	Cat Swarm Optimization	CSO	OriginalCSO	2006	11	hard
Swarm	Artificial Bee Colony	ABC	OriginalABC	2007	8	medium
Swarm	Ant Colony Optimization	ACOR	OriginalACOR	2008	5	easy
Swarm	Cuckoo Search Algorithm	CSA	OriginalCSA	2009	3	medium
Swarm	Firefly Algorithm	FFA	OriginalFFA	2009	8	easy
Swarm	Fireworks Algorithm	FA	OriginalFA	2010	7	medium
Swarm	Bat Algorithm	BA	OriginalBA	2010	6	medium
Swarm	*	*	AdaptiveBA	2010	8	medium
Swarm	*	*	ModifiedBA	*	5	medium
Swarm	Fruit-fly Optimization Algorithm	FOA	OriginalFOA	2012	2	easy
Swarm	*	*	BaseFOA	*	2	easy
Swarm	*	*	WhaleFOA	2020	2	medium
Swarm	Social Spider Optimization	SSpiderO	OriginalSSpiderO	2018	4	hard*
Swarm	Grey Wolf Optimizer	GWO	OriginalGWO	2014	2	easy
Swarm	*	*	RW_GWO	2019	2	easy
Swarm	Social Spider Algorithm	SSpiderA	OriginalSSpiderA	2015	5	medium
Swarm	Ant Lion Optimizer	ALO	OriginalALO	2015	2	easy
Swarm	*	*	BaseALO	*	2	easy
Swarm	Moth Flame Optimization	MFO	OriginalMFO	2015	2	easy
Swarm	*	*	BaseMFO	*	2	easy
Swarm	Elephant Herding Optimization	EHO	OriginalEHO	2015	5	easy
Swarm	Jaya Algorithm	JA	OriginalJA	2016	2	easy
Swarm	*	*	BaseJA	*	2	easy
Swarm	*	*	LevyJA	2021	2	easy
Swarm	Whale Optimization Algorithm	WOA	OriginalWOA	2016	2	medium
Swarm	*	*	HI_WOA	2019	3	medium
Swarm	Dragonfly Optimization	DO	OriginalDO	2016	2	medium
Swarm	Bird Swarm Algorithm	BSA	OriginalBSA	2016	9	medium
Swarm	Spotted Hyena Optimizer	SHO	OriginalSHO	2017	4	medium
Swarm	Salp Swarm Optimization	SSO	OriginalSSO	2017	2	easy
Swarm	Swarm Robotics Search And Rescue	SRSR	OriginalSRSR	2017	2	hard*
Swarm	Grasshopper Optimisation Algorithm	GOA	OriginalGOA	2017	4	easy
Swarm	Coyote Optimization Algorithm	COA	OriginalCOA	2018	3	medium
Swarm	Moth Search Algorithm	MSA	OriginalMSA	2018	5	easy
Swarm	Sea Lion Optimization	SLO	OriginalSLO	2019	2	medium
Swarm	*	*	ModifiedSLO	*	2	medium
Swarm	*	*	ImprovedSLO	2022	4	medium
Swarm	Nake Mole*Rat Algorithm	NMRA	OriginalNMRA	2019	3	easy
Swarm	*	*	ImprovedNMRA	*	4	medium
Swarm	Pathfinder Algorithm	PFA	OriginalPFA	2019	2	medium
Swarm	Sailfish Optimizer	SFO	OriginalSFO	2019	5	easy
Swarm	*	*	ImprovedSFO	*	3	medium
Swarm	Harris Hawks Optimization	HHO	OriginalHHO	2019	2	medium
Swarm	Manta Ray Foraging Optimization	MRFO	OriginalMRFO	2020	3	medium
Swarm	Bald Eagle Search	BES	OriginalBES	2020	7	easy
Swarm	Sparrow Search Algorithm	SSA	OriginalSSA	2020	5	medium
Swarm	*	*	BaseSSA	*	5	medium
Swarm	Hunger Games Search	HGS	OriginalHGS	2021	4	medium
Swarm	Aquila Optimizer	AO	OriginalAO	2021	2	easy
Swarm	Hybrid Grey Wolf * Whale Optimization Algorithm	GWO	GWO_WOA	2022	2	easy
Swarm	Marine Predators Algorithm	MPA	OriginalMPA	2020	2	medium
Swarm	Honey Badger Algorithm	HBA	OriginalHBA	2022	2	easy
Swarm	Sand Cat Swarm Optimization	SCSO	OriginalSCSO	2022	2	easy
Swarm	Tuna Swarm Optimization	TSO	OriginalTSO	2021	2	medium
Swarm	African Vultures Optimization Algorithm	AVOA	OriginalAVOA	2022	7	medium
Swarm	Artificial Gorilla Troops Optimization	AGTO	OriginalAGTO	2021	5	medium
Swarm	*	*	MGTO	2023	3	medium
Swarm	Artificial Rabbits Optimization	ARO	OriginalARO	2022	2	easy
Swarm	*	*	LARO	2022	2	easy
Swarm	*	*	IARO	2022	2	easy
Swarm	Egret Swarm Optimization Algorithm	ESOA	OriginalESOA	2022	2	medium
Swarm	Fox Optimizer	FOX	OriginalFOX	2023	4	easy
Swarm	Golden Jackal Optimization	GJO	OriginalGJO	2022	2	easy
Swarm	Giant Trevally Optimization	GTO	OriginalGTO	2022	4	medium
Swarm	*	*	Matlab101GTO	2022	2	medium
Swarm	*	*	Matlab102GTO	2023	2	hard
Swarm	Mountain Gazelle Optimizer	MGO	OriginalMGO	2022	2	easy
Swarm	Sea-Horse Optimization	SeaHO	OriginalSeaHO	2022	2	medium
***	***	***	***	***	***	***
Physics	Simulated Annealling	SA	OriginalSA	1983	9	medium
Physics	*	*	GaussianSA	*	5	medium
Physics	*	*	SwarmSA	1987	9	medium
Physics	Wind Driven Optimization	WDO	OriginalWDO	2013	7	easy
Physics	Multi*Verse Optimizer	MVO	OriginalMVO	2016	4	easy
Physics	*	*	BaseMVO	*	4	easy
Physics	Tug of War Optimization	TWO	OriginalTWO	2016	2	easy
Physics	*	*	OppoTWO	*	2	medium
Physics	*	*	LevyTWO	*	2	medium
Physics	*	*	EnhancedTWO	2020	2	medium
Physics	Electromagnetic Field Optimization	EFO	OriginalEFO	2016	6	easy
Physics	*	*	BaseEFO	*	6	medium
Physics	Nuclear Reaction Optimization	NRO	OriginalNRO	2019	2	hard*
Physics	Henry Gas Solubility Optimization	HGSO	OriginalHGSO	2019	3	medium
Physics	Atom Search Optimization	ASO	OriginalASO	2019	4	medium
Physics	Equilibrium Optimizer	EO	OriginalEO	2019	2	easy
Physics	*	*	ModifiedEO	2020	2	medium
Physics	*	*	AdaptiveEO	2020	2	medium
Physics	Archimedes Optimization Algorithm	ArchOA	OriginalArchOA	2021	8	medium
Physics	Chernobyl Disaster Optimization	CDO	OriginalCDO	2023	2	easy
Physics	Energy Valley Optimization	EVO	OriginalEVO	2023	2	medium
Physics	Fick's Law Algorithm	FLA	OriginalFLA	2023	8	hard
Physics	Physical Phenomenon of RIME-ice	RIME	OriginalRIME	2023	3	easy
***	***	***	***	***	***	***
Human	Culture Algorithm	CA	OriginalCA	1994	3	easy
Human	Imperialist Competitive Algorithm	ICA	OriginalICA	2007	8	hard*
Human	Teaching Learning*based Optimization	TLO	OriginalTLO	2011	2	easy
Human	*	*	BaseTLO	2012	2	easy
Human	*	*	ITLO	2013	3	medium
Human	Brain Storm Optimization	BSO	OriginalBSO	2011	8	medium
Human	*	*	ImprovedBSO	2017	7	medium
Human	Queuing Search Algorithm	QSA	OriginalQSA	2019	2	hard
Human	*	*	BaseQSA	*	2	hard
Human	*	*	OppoQSA	*	2	hard
Human	*	*	LevyQSA	*	2	hard
Human	*	*	ImprovedQSA	2021	2	hard
Human	Search And Rescue Optimization	SARO	OriginalSARO	2019	4	medium
Human	*	*	BaseSARO	*	4	medium
Human	Life Choice*Based Optimization	LCO	OriginalLCO	2019	3	easy
Human	*	*	BaseLCO	*	3	easy
Human	*	*	ImprovedLCO	*	2	easy
Human	Social Ski*Driver Optimization	SSDO	OriginalSSDO	2019	2	easy
Human	Gaining Sharing Knowledge*based Algorithm	GSKA	OriginalGSKA	2019	6	medium
Human	*	*	BaseGSKA	*	4	medium
Human	Coronavirus Herd Immunity Optimization	CHIO	OriginalCHIO	2020	4	medium
Human	*	*	BaseCHIO	*	4	medium
Human	Forensic*Based Investigation Optimization	FBIO	OriginalFBIO	2020	2	medium
Human	*	*	BaseFBIO	*	2	medium
Human	Battle Royale Optimization	BRO	OriginalBRO	2020	3	medium
Human	*	*	BaseBRO	*	3	medium
Human	Student Psychology Based Optimization	SPBO	OriginalSPBO	2020	2	medium
Human	*	*	DevSPBO	*	2	medium
Human	Heap-based Optimization	HBO	OriginalHBO	2020	3	medium
Human	Human Conception Optimization	HCO	OriginalHCO	2022	6	medium
Human	Dwarf Mongoose Optimization Algorithm	DMOA	OriginalDMOA	2022	4	medium
Human	*	*	DevDMOA	*	3	medium
Human	War Strategy Optimization	WarSO	OriginalWarSO	2022	3	easy
***	***	***	***	***	***	***
Bio	Invasive Weed Optimization	IWO	OriginalIWO	2006	7	easy
Bio	Biogeography*Based Optimization	BBO	OriginalBBO	2008	4	easy
Bio	*	*	BaseBBO	*	4	easy
Bio	Virus Colony Search	VCS	OriginalVCS	2016	4	hard*
Bio	*	*	BaseVCS	*	4	hard*
Bio	Satin Bowerbird Optimizer	SBO	OriginalSBO	2017	5	easy
Bio	*	*	BaseSBO	*	5	easy
Bio	Earthworm Optimisation Algorithm	EOA	OriginalEOA	2018	8	medium
Bio	Wildebeest Herd Optimization	WHO	OriginalWHO	2019	12	hard
Bio	Slime Mould Algorithm	SMA	OriginalSMA	2020	3	easy
Bio	*	*	BaseSMA	*	3	easy
Bio	Barnacles Mating Optimizer	BMO	OriginalBMO	2018	3	easy
Bio	Tunicate Swarm Algorithm	TSA	OriginalTSA	2020	2	easy
Bio	Symbiotic Organisms Search	SOS	OriginalSOS	2014	2	medium
Bio	Seagull Optimization Algorithm	SOA	OriginalSOA	2019	3	easy
Bio	*	*	DevSOA	*	3	easy
Bio	Brown-Bear Optimization Algorithm	BBOA	OriginalBBOA	2023	2	medium
Bio	Tree Physiology Optimization	TPO	OriginalTPO	2017	5	medium
***	***	***	***	***	***	***
System	Germinal Center Optimization	GCO	OriginalGCO	2018	4	medium
System	*	*	BaseGCO	*	4	medium
System	Water Cycle Algorithm	WCA	OriginalWCA	2012	5	medium
System	Artificial Ecosystem*based Optimization	AEO	OriginalAEO	2019	2	easy
System	*	*	EnhancedAEO	2020	2	medium
System	*	*	ModifiedAEO	2020	2	medium
System	*	*	ImprovedAEO	2021	2	medium
System	*	*	AugmentedAEO	2022	2	medium
***	***	***	***	***	***	***
Math	Hill Climbing	HC	OriginalHC	1993	3	easy
Math	*	*	SwarmHC	*	3	easy
Math	Cross-Entropy Method	CEM	OriginalCEM	1997	4	easy
Math	Tabu Search	TS	OriginalTS	2004	5	easy
Math	Sine Cosine Algorithm	SCA	OriginalSCA	2016	2	easy
Math	*	*	BaseSCA	*	2	easy
Math	*	*	QLE-SCA	2022	4	hard
Math	Gradient-Based Optimizer	GBO	OriginalGBO	2020	5	medium
Math	Arithmetic Optimization Algorithm	AOA	OrginalAOA	2021	6	easy
Math	Chaos Game Optimization	CGO	OriginalCGO	2021	2	easy
Math	Pareto-like Sequential Sampling	PSS	OriginalPSS	2021	4	medium
Math	weIghted meaN oF vectOrs	INFO	OriginalINFO	2022	2	medium
Math	RUNge Kutta optimizer	RUN	OriginalRUN	2021	2	hard
Math	Circle Search Algorithm	CircleSA	OriginalCircleSA	2022	3	easy
Math	Success History Intelligent Optimization	SHIO	OriginalSHIO	2022	2	easy
***	***	***	***	***	***	***
Music	Harmony Search	HS	OriginalHS	2001	4	easy
Music	*	*	BaseHS	*	4	easy
+++	+++	+++	+++	+++	+++	+++
WARNING	PLEASE CHECK PLAGIARISM BEFORE USING BELOW ALGORITHMS	*	*	*	*	*
Swarm	Coati Optimization Algorithm	CoatiOA	OriginalCoatiOA	2023	2	easy
Swarm	Fennec For Optimization	FFO	OriginalFFO	2022	2	easy
Swarm	Northern Goshawk Optimization	NGO	OriginalNGO	2021	2	easy
Swarm	Osprey Optimization Algorithm	OOA	OriginalOOA	2023	2	easy
Swarm	Pelican Optimization Algorithm	POA	OriginalPOA	2023	2	easy
Swarm	Serval Optimization Algorithm	ServalOA	OriginalServalOA	2022	2	easy
Swarm	Siberian Tiger Optimization	STO	OriginalSTO	2022	2	easy
Swarm	Tasmanian Devil Optimization	TDO	OriginalTDO	2022	2	easy
Swarm	Walrus Optimization Algorithm	WaOA	OriginalWaOA	2022	2	easy
Swarm	Zebra Optimization Algorithm	ZOA	OriginalZOA	2022	2	easy
Human	Teamwork Optimization Algorithm	TOA	OriginalTOA	2021	2	easy

)

Group	Name	Module	Class	Year	Paras	Difficulty
Evolutionary	Evolutionary Programming	EP	OriginalEP	1964	3	easy
Evolutionary	*	*	LevyEP	*	3	easy
Evolutionary	Evolution Strategies	ES	OriginalES	1971	3	easy
Evolutionary	*	*	LevyES	*	3	easy
Evolutionary	*	*	CMA_ES	2003	2	hard
Evolutionary	*	*	Simple_CMA_ES	2023	2	medium
Evolutionary	Memetic Algorithm	MA	OriginalMA	1989	7	easy
Evolutionary	Genetic Algorithm	GA	BaseGA	1992	4	easy
Evolutionary	*	*	SingleGA	*	7	easy
Evolutionary	*	*	MultiGA	*	7	easy
Evolutionary	*	*	EliteSingleGA	*	10	easy
Evolutionary	*	*	EliteMultiGA	*	10	easy
Evolutionary	Differential Evolution	DE	BaseDE	1997	5	easy
Evolutionary	*	*	JADE	2009	6	medium
Evolutionary	*	*	SADE	2005	2	medium
Evolutionary	*	*	SAP_DE	2006	3	medium
Evolutionary	Success-History Adaptation Differential Evolution	SHADE	OriginalSHADE	2013	4	medium
Evolutionary	*	*	L_SHADE	2014	4	medium
Evolutionary	Flower Pollination Algorithm	FPA	OriginalFPA	2014	4	medium
Evolutionary	Coral Reefs Optimization	CRO	OriginalCRO	2014	11	medium
Evolutionary	*	*	OCRO	2019	12	medium
***	***	***	***	***	***	***
Swarm	Particle Swarm Optimization	PSO	OriginalPSO	1995	6	easy
Swarm	*	*	PPSO	2019	2	medium
Swarm	*	*	HPSO_TVAC	2017	4	medium
Swarm	*	*	C_PSO	2015	6	medium
Swarm	*	*	CL_PSO	2006	6	medium
Swarm	Bacterial Foraging Optimization	BFO	OriginalBFO	2002	10	hard
Swarm	*	*	ABFO	2019	8	medium
Swarm	Bees Algorithm	BeesA	OriginalBeesA	2005	8	medium
Swarm	*	*	ProbBeesA	2015	5	medium
Swarm	*	*	CleverBookBeesA	2006	8	medium
Swarm	Cat Swarm Optimization	CSO	OriginalCSO	2006	11	hard
Swarm	Artificial Bee Colony	ABC	OriginalABC	2007	8	medium
Swarm	Ant Colony Optimization	ACOR	OriginalACOR	2008	5	easy
Swarm	Cuckoo Search Algorithm	CSA	OriginalCSA	2009	3	medium
Swarm	Firefly Algorithm	FFA	OriginalFFA	2009	8	easy
Swarm	Fireworks Algorithm	FA	OriginalFA	2010	7	medium
Swarm	Bat Algorithm	BA	OriginalBA	2010	6	medium
Swarm	*	*	AdaptiveBA	2010	8	medium
Swarm	*	*	ModifiedBA	*	5	medium
Swarm	Fruit-fly Optimization Algorithm	FOA	OriginalFOA	2012	2	easy
Swarm	*	*	BaseFOA	*	2	easy
Swarm	*	*	WhaleFOA	2020	2	medium
Swarm	Social Spider Optimization	SSpiderO	OriginalSSpiderO	2018	4	hard*
Swarm	Spider Monkey Optimization	SMO	DevSMO	2014	4	hard
Swarm	Grey Wolf Optimizer	GWO	OriginalGWO	2014	2	easy
Swarm	*	*	RW_GWO	2019	2	easy
Swarm	*	*	GWO_WOA	2022	2	easy
Swarm	*	*	IGWO	2018	4	easy
Swarm	*	*	ChaoticGWO	2018	4	easy
Swarm	*	*	FuzzyGWO	2017	3	medium
Swarm	*	*	IncrementalGWO	2021	3	medium
Swarm	*	*	ExGWO	2021	2	medium
Swarm	*	*	DS_GWO	2022	4	medium
Swarm	*	*	IOBL_GWO	2021	2	medium
Swarm	*	*	OGWO	2021	4	medium
Swarm	*	*	ER_GWO	2020	5	medium
Swarm	*	*	CG_GWO	2022	2	hard
Swarm	Social Spider Algorithm	SSpiderA	OriginalSSpiderA	2015	5	medium
Swarm	Ant Lion Optimizer	ALO	OriginalALO	2015	2	easy
Swarm	*	*	BaseALO	*	2	easy
Swarm	Moth Flame Optimization	MFO	OriginalMFO	2015	2	easy
Swarm	*	*	BaseMFO	*	2	easy
Swarm	Elephant Herding Optimization	EHO	OriginalEHO	2015	5	easy
Swarm	Jaya Algorithm	JA	OriginalJA	2016	2	easy
Swarm	*	*	BaseJA	*	2	easy
Swarm	*	*	LevyJA	2021	2	easy
Swarm	Whale Optimization Algorithm	WOA	OriginalWOA	2016	2	medium
Swarm	*	*	HI_WOA	2019	3	medium
Swarm	Dragonfly Optimization	DO	OriginalDO	2016	2	medium
Swarm	Bird Swarm Algorithm	BSA	OriginalBSA	2016	9	medium
Swarm	Spotted Hyena Optimizer	SHO	OriginalSHO	2017	4	medium
Swarm	Salp Swarm Optimization	SSO	OriginalSSO	2017	2	easy
Swarm	Swarm Robotics Search And Rescue	SRSR	OriginalSRSR	2017	2	hard*
Swarm	Grasshopper Optimisation Algorithm	GOA	OriginalGOA	2017	4	easy
Swarm	Coyote Optimization Algorithm	COA	OriginalCOA	2018	3	medium
Swarm	Moth Search Algorithm	MSA	OriginalMSA	2018	5	easy
Swarm	Squirrel Search Algorithm	SquirrelSA	OriginalSquirrelSA	2019	7	medium
Swarm	Fitness Dependent Optimizer	FDO	OriginalFDO	2019	3	medium
Swarm	Sea Lion Optimization	SLO	OriginalSLO	2019	2	medium
Swarm	*	*	ModifiedSLO	*	2	medium
Swarm	*	*	ImprovedSLO	2022	4	medium
Swarm	Emperor Penguins Colony/td>	EPC	DevEPC	2019	6	hard
Swarm	Nake Mole*Rat Algorithm	NMRA	OriginalNMRA	2019	3	easy
Swarm	*	*	ImprovedNMRA	*	4	medium
Swarm	Pathfinder Algorithm	PFA	OriginalPFA	2019	2	medium
Swarm	Sailfish Optimizer	SFO	OriginalSFO	2019	5	easy
Swarm	*	*	ImprovedSFO	*	3	medium
Swarm	Harris Hawks Optimization	HHO	OriginalHHO	2019	2	medium
Swarm	Manta Ray Foraging Optimization	MRFO	OriginalMRFO	2020	3	medium
Swarm	Bald Eagle Search	BES	OriginalBES	2020	7	easy
Swarm	Sparrow Search Algorithm	SSA	OriginalSSA	2020	5	medium
Swarm	*	*	BaseSSA	*	5	medium
Swarm	Hunger Games Search	HGS	OriginalHGS	2021	4	medium
Swarm	Aquila Optimizer	AO	OriginalAO	2021	2	easy
Swarm	*	*	AAO	2024	4	easy
Swarm	Hybrid Grey Wolf * Whale Optimization Algorithm	GWO	GWO_WOA	2022	2	easy
Swarm	Marine Predators Algorithm	MPA	OriginalMPA	2020	2	medium
Swarm	Honey Badger Algorithm	HBA	OriginalHBA	2022	2	easy
Swarm	Sand Cat Swarm Optimization	SCSO	OriginalSCSO	2022	2	easy
Swarm	Tuna Swarm Optimization	TSO	OriginalTSO	2021	2	medium
Swarm	African Vultures Optimization Algorithm	AVOA	OriginalAVOA	2022	7	medium
Swarm	Artificial Gorilla Troops Optimization	AGTO	OriginalAGTO	2021	5	medium
Swarm	*	*	MGTO	2023	3	medium
Swarm	Artificial Rabbits Optimization	ARO	OriginalARO	2022	2	easy
Swarm	*	*	LARO	2022	2	easy
Swarm	*	*	IARO	2022	2	easy
Swarm	Egret Swarm Optimization Algorithm	ESOA	OriginalESOA	2022	2	medium
Swarm	Fox Optimizer	FOX	OriginalFOX	2023	4	easy
Swarm	Golden Jackal Optimization	GJO	OriginalGJO	2022	2	easy
Swarm	Giant Trevally Optimization	GTO	OriginalGTO	2022	4	medium
Swarm	*	*	Matlab101GTO	2022	2	medium
Swarm	*	*	Matlab102GTO	2023	2	hard
Swarm	Mountain Gazelle Optimizer	MGO	OriginalMGO	2022	2	easy
Swarm	Sea-Horse Optimization	SeaHO	OriginalSeaHO	2022	2	medium
***	***	***	***	***	***	***
Physics	Simulated Annealling	SA	OriginalSA	1983	9	medium
Physics	*	*	GaussianSA	*	5	medium
Physics	*	*	SwarmSA	1987	9	medium
Physics	Wind Driven Optimization	WDO	OriginalWDO	2013	7	easy
Physics	Multi*Verse Optimizer	MVO	OriginalMVO	2016	4	easy
Physics	*	*	BaseMVO	*	4	easy
Physics	Tug of War Optimization	TWO	OriginalTWO	2016	2	easy
Physics	*	*	OppoTWO	*	2	medium
Physics	*	*	LevyTWO	*	2	medium
Physics	*	*	EnhancedTWO	2020	2	medium
Physics	Electromagnetic Field Optimization	EFO	OriginalEFO	2016	6	easy
Physics	*	*	BaseEFO	*	6	medium
Physics	Nuclear Reaction Optimization	NRO	OriginalNRO	2019	2	hard*
Physics	Henry Gas Solubility Optimization	HGSO	OriginalHGSO	2019	3	medium
Physics	Atom Search Optimization	ASO	OriginalASO	2019	4	medium
Physics	Equilibrium Optimizer	EO	OriginalEO	2019	2	easy
Physics	*	*	ModifiedEO	2020	2	medium
Physics	*	*	AdaptiveEO	2020	2	medium
Physics	Archimedes Optimization Algorithm	ArchOA	OriginalArchOA	2021	8	medium
Physics	Chernobyl Disaster Optimization	CDO	OriginalCDO	2023	2	easy
Physics	Energy Valley Optimization	EVO	OriginalEVO	2023	2	medium
Physics	Fick's Law Algorithm	FLA	OriginalFLA	2023	8	hard
Physics	Physical Phenomenon of RIME-ice	RIME	OriginalRIME	2023	3	easy
Physics	Electrical Storm Optimization	ESO	OriginalESO	2025	2	hard
***	***	***	***	***	***	***
Human	Culture Algorithm	CA	OriginalCA	1994	3	easy
Human	Imperialist Competitive Algorithm	ICA	OriginalICA	2007	8	hard*
Human	Teaching Learning*based Optimization	TLO	OriginalTLO	2011	2	easy
Human	*	*	BaseTLO	2012	2	easy
Human	*	*	ITLO	2013	3	medium
Human	Brain Storm Optimization	BSO	OriginalBSO	2011	8	medium
Human	*	*	ImprovedBSO	2017	7	medium
Human	Queuing Search Algorithm	QSA	OriginalQSA	2019	2	hard
Human	*	*	BaseQSA	*	2	hard
Human	*	*	OppoQSA	*	2	hard
Human	*	*	LevyQSA	*	2	hard
Human	*	*	ImprovedQSA	2021	2	hard
Human	Search And Rescue Optimization	SARO	OriginalSARO	2019	4	medium
Human	*	*	BaseSARO	*	4	medium
Human	Life Choice*Based Optimization	LCO	OriginalLCO	2019	3	easy
Human	*	*	BaseLCO	*	3	easy
Human	*	*	ImprovedLCO	*	2	easy
Human	Social Ski*Driver Optimization	SSDO	OriginalSSDO	2019	2	easy
Human	Gaining Sharing Knowledge*based Algorithm	GSKA	OriginalGSKA	2019	6	medium
Human	*	*	BaseGSKA	*	4	medium
Human	Coronavirus Herd Immunity Optimization	CHIO	OriginalCHIO	2020	4	medium
Human	*	*	BaseCHIO	*	4	medium
Human	Forensic*Based Investigation Optimization	FBIO	OriginalFBIO	2020	2	medium
Human	*	*	BaseFBIO	*	2	medium
Human	Battle Royale Optimization	BRO	OriginalBRO	2020	3	medium
Human	*	*	BaseBRO	*	3	medium
Human	Student Psychology Based Optimization	SPBO	OriginalSPBO	2020	2	medium
Human	*	*	DevSPBO	*	2	medium
Human	Heap-based Optimization	HBO	OriginalHBO	2020	3	medium
Human	Human Conception Optimization	HCO	OriginalHCO	2022	6	medium
Human	Ali baba and the Forty Thieves	AFT	OriginalAFT	2022	2	easy
Human	Child Drawing Development Optimization	CDDO	OriginalCDDO	2022	4	easy
Human	Dwarf Mongoose Optimization Algorithm	DMOA	OriginalDMOA	2022	4	medium
Human	*	*	DevDMOA	*	3	medium
Human	War Strategy Optimization	WarSO	OriginalWarSO	2022	3	easy
***	***	***	***	***	***	***
Bio	Invasive Weed Optimization	IWO	OriginalIWO	2006	7	easy
Bio	Biogeography*Based Optimization	BBO	OriginalBBO	2008	4	easy
Bio	*	*	BaseBBO	*	4	easy
Bio	Virus Colony Search	VCS	OriginalVCS	2016	4	hard*
Bio	*	*	BaseVCS	*	4	hard*
Bio	Satin Bowerbird Optimizer	SBO	OriginalSBO	2017	5	easy
Bio	*	*	BaseSBO	*	5	easy
Bio	Earthworm Optimisation Algorithm	EOA	OriginalEOA	2018	8	medium
Bio	Wildebeest Herd Optimization	WHO	OriginalWHO	2019	12	hard
Bio	Slime Mould Algorithm	SMA	OriginalSMA	2020	3	easy
Bio	*	*	BaseSMA	*	3	easy
Bio	Barnacles Mating Optimizer	BMO	OriginalBMO	2018	3	easy
Bio	Tunicate Swarm Algorithm	TSA	OriginalTSA	2020	2	easy
Bio	Symbiotic Organisms Search	SOS	OriginalSOS	2014	2	medium
Bio	Seagull Optimization Algorithm	SOA	OriginalSOA	2019	3	easy
Bio	*	*	DevSOA	*	3	easy
Bio	Brown-Bear Optimization Algorithm	BBOA	OriginalBBOA	2023	2	medium
Bio	Tree Physiology Optimization	TPO	OriginalTPO	2017	5	medium
***	***	***	***	***	***	***
System	Germinal Center Optimization	GCO	OriginalGCO	2018	4	medium
System	*	*	BaseGCO	*	4	medium
System	Water Cycle Algorithm	WCA	OriginalWCA	2012	5	medium
System	Artificial Ecosystem*based Optimization	AEO	OriginalAEO	2019	2	easy
System	*	*	EnhancedAEO	2020	2	medium
System	*	*	ModifiedAEO	2020	2	medium
System	*	*	ImprovedAEO	2021	2	medium
System	*	*	AugmentedAEO	2022	2	medium
***	***	***	***	***	***	***
Math	Hill Climbing	HC	OriginalHC	1993	3	easy
Math	*	*	SwarmHC	*	3	easy
Math	Cross-Entropy Method	CEM	OriginalCEM	1997	4	easy
Math	Tabu Search	TS	OriginalTS	2004	5	easy
Math	Sine Cosine Algorithm	SCA	OriginalSCA	2016	2	easy
Math	*	*	BaseSCA	*	2	easy
Math	*	*	QLE-SCA	2022	4	hard
Math	Gradient-Based Optimizer	GBO	OriginalGBO	2020	5	medium
Math	Arithmetic Optimization Algorithm	AOA	OrginalAOA	2021	6	easy
Math	Chaos Game Optimization	CGO	OriginalCGO	2021	2	easy
Math	Pareto-like Sequential Sampling	PSS	OriginalPSS	2021	4	medium
Math	weIghted meaN oF vectOrs	INFO	OriginalINFO	2022	2	medium
Math	RUNge Kutta optimizer	RUN	OriginalRUN	2021	2	hard
Math	Circle Search Algorithm	CircleSA	OriginalCircleSA	2022	3	easy
Math	Success History Intelligent Optimization	SHIO	OriginalSHIO	2022	2	easy
***	***	***	***	***	***	***
Music	Harmony Search	HS	OriginalHS	2001	4	easy
Music	*	*	BaseHS	*	4	easy
SOTA	Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood	LSHADEcnEpSin	OriginalLSHADEcnEpSin	2017	9	hard
SOTA	Improved Multi-operator Differential Evolution Algorithm	IMODE	OriginalIMODE	2020	4	hard

### ❌ Warning: Algorithms Suspected of Plagiarism During our implementation and classification of metaheuristic optimization algorithms, we identified a set of methods that raise serious concerns regarding **scientific integrity and originality**. These algorithms are typically published under **different names**, but they appear to share: - The **same core mathematical models**, equations, and update rules. - Only superficial changes in naming, metaphors, or biological analogies. - Publications authored by **the same or overlapping research groups**. - **Heavy criticism** on public academic forums such as [PubPeer](https://pubpeer.com), where many of these papers are flagged for **self-plagiarism**, **redundant publication**, or **lack of novelty**. - Some of these papers may be **withdrawn or retracted in the future**, as investigations unfold. For these reasons, we strongly advise the **exclusion** of the following algorithms from scientific benchmarking, comparative studies, or any applications unless their originality is transparently validated. **I have personally implemented these algorithms, which is why I can confidently say that they are nearly identical and likely cases of plagiarism. For this reason, I will no longer spend time coding such algorithms in the future. This warning is intended to help others avoid using or relying on these methods in their work.**

Group	Name	Module	Class	Year	Paras	Difficulty
Swarm	Coati Optimization Algorithm	CoatiOA	OriginalCoatiOA	2023	2	easy
Swarm	Fennec For Optimization	FFO	OriginalFFO	2022	2	easy
Swarm	Northern Goshawk Optimization	NGO	OriginalNGO	2021	2	easy
Swarm	Osprey Optimization Algorithm	OOA	OriginalOOA	2023	2	easy
Swarm	Pelican Optimization Algorithm	POA	OriginalPOA	2023	2	easy
Swarm	Serval Optimization Algorithm	ServalOA	OriginalServalOA	2022	2	easy
Swarm	Siberian Tiger Optimization	STO	OriginalSTO	2022	2	easy
Swarm	Tasmanian Devil Optimization	TDO	OriginalTDO	2022	2	easy
Swarm	Walrus Optimization Algorithm	WaOA	OriginalWaOA	2022	2	easy
Swarm	Zebra Optimization Algorithm	ZOA	OriginalZOA	2022	2	easy
Human	Teamwork Optimization Algorithm	TOA	OriginalTOA	2021	2	easy

### ⚠️ Ethical Reminder Researchers and students are urged to **exercise caution** when referencing or applying the algorithms listed above. Using unoriginal or unethical work can compromise the **scientific credibility** of any downstream research and introduce **misleading experimental results**. > 🔗 **Check [PubPeer1](https://pubpeer.com/publications/1F5DCE5BC42BF2D77A1B0C281A5295)** and [PubPeer2](https://pubpeer.com/publications/D47357D409AE273F9E03C7CBE30EB7) to > find ongoing discussions and critiques from the academic community. ---

💻 Define All Optimizers

```python from mealpy import (StringVar, FloatVar, BoolVar, PermutationVar, CategoricalVar, IntegerVar, BinaryVar, TransferBinaryVar, TransferBoolVar) from mealpy import Tuner, Multitask, Problem, Optimizer, Termination, ParameterGrid from mealpy import get_all_optimizers, get_optimizer_by_name from mealpy import BBO, PSO, GA, ALO, AO, ARO, AVOA, BA, BBOA, BMO, EOA, IWO from mealpy import GJO, FOX, FOA, FFO, FFA, FA, ESOA, EHO, DO, DMOA, CSO, CSA, CoatiOA, COA, BSA from mealpy import HCO, ICA, LCO, WarSO, TOA, TLO, SSDO, SPBO, SARO, QSA, ArchOA, ASO, CDO, EFO, EO, EVO, FLA from mealpy import HGSO, MVO, NRO, RIME, SA, WDO, TWO, ABC, ACOR, AGTO, BeesA, BES, BFO, ZOA, WOA, WaOA, TSO from mealpy import PFA, OOA, NGO, NMRA, MSA, MRFO, MPA, MGO, MFO, JA, HHO, HGS, HBA, GWO, GTO, GOA from mealpy import SBO, SMA, SOA, SOS, TPO, TSA, VCS, WHO, AOA, CEM, CGO, CircleSA, GBO, HC, INFO, PSS, RUN, SCA from mealpy import SHIO, TS, HS, AEO, GCO, WCA, CRO, DE, EP, ES, FPA, MA, SHADE, BRO, BSO, CA, CHIO, FBIO, GSKA, HBO from mealpy import TDO, STO, SSpiderO, SSpiderA, SSO, SSA, SRSR, SLO, SHO, SFO, ServalOA, SeaHO, SCSO, POA ## Newly added module in version 3.0.3 from mealpy import ESO, EPC, SMO, AFT, CDDO, SquirrelSA, FDO, LSHADEcnEpSin, IMODE if __name__ == "__main__": model = BBO.OriginalBBO(epoch=10, pop_size=30, p_m=0.01, n_elites=2) model = PSO.OriginalPSO(epoch=100, pop_size=50, c1=2.05, c2=20.5, w=0.4) model = PSO.LDW_PSO(epoch=100, pop_size=50, c1=2.05, c2=20.5, w_min=0.4, w_max=0.9) model = PSO.AIW_PSO(epoch=100, pop_size=50, c1=2.05, c2=20.5, alpha=0.4) model = PSO.P_PSO(epoch=100, pop_size=50) model = PSO.HPSO_TVAC(epoch=100, pop_size=50, ci=0.5, cf=0.1) model = PSO.C_PSO(epoch=100, pop_size=50, c1=2.05, c2=2.05, w_min=0.4, w_max=0.9) model = PSO.CL_PSO(epoch=100, pop_size=50, c_local=1.2, w_min=0.4, w_max=0.9, max_flag=7) model = GA.BaseGA(epoch=100, pop_size=50, pc=0.9, pm=0.05, selection="tournament", k_way=0.4, crossover="multi_points", mutation="swap") model = GA.SingleGA(epoch=100, pop_size=50, pc=0.9, pm=0.8, selection="tournament", k_way=0.4, crossover="multi_points", mutation="swap") model = GA.MultiGA(epoch=100, pop_size=50, pc=0.9, pm=0.8, selection="tournament", k_way=0.4, crossover="multi_points", mutation="swap") model = GA.EliteSingleGA(epoch=100, pop_size=50, pc=0.95, pm=0.8, selection="roulette", crossover="uniform", mutation="swap", k_way=0.2, elite_best=0.1, elite_worst=0.3, strategy=0) model = GA.EliteMultiGA(epoch=100, pop_size=50, pc=0.95, pm=0.8, selection="roulette", crossover="uniform", mutation="swap", k_way=0.2, elite_best=0.1, elite_worst=0.3, strategy=0) model = ABC.OriginalABC(epoch=1000, pop_size=50, n_limits=50) model = ACOR.OriginalACOR(epoch=1000, pop_size=50, sample_count=25, intent_factor=0.5, zeta=1.0) model = AGTO.OriginalAGTO(epoch=1000, pop_size=50, p1=0.03, p2=0.8, beta=3.0) model = AGTO.MGTO(epoch=1000, pop_size=50, pp=0.03) model = ALO.OriginalALO(epoch=100, pop_size=50) model = ALO.DevALO(epoch=100, pop_size=50) model = AO.OriginalAO(epoch=100, pop_size=50) model = ARO.OriginalARO(epoch=100, pop_size=50) model = ARO.LARO(epoch=100, pop_size=50) model = ARO.IARO(epoch=100, pop_size=50) model = AVOA.OriginalAVOA(epoch=100, pop_size=50, p1=0.6, p2=0.4, p3=0.6, alpha=0.8, gama=2.5) model = BA.OriginalBA(epoch=100, pop_size=50, loudness=0.8, pulse_rate=0.95, pf_min=0.1, pf_max=10.0) model = BA.AdaptiveBA(epoch=100, pop_size=50, loudness_min=1.0, loudness_max=2.0, pr_min=-2.5, pr_max=0.85, pf_min=0.1, pf_max=10.) model = BA.DevBA(epoch=100, pop_size=50, pulse_rate=0.95, pf_min=0., pf_max=10.) model = BBOA.OriginalBBOA(epoch=100, pop_size=50) model = BMO.OriginalBMO(epoch=100, pop_size=50, pl=4) model = EOA.OriginalEOA(epoch=100, pop_size=50, p_c=0.9, p_m=0.01, n_best=2, alpha=0.98, beta=0.9, gama=0.9) model = IWO.OriginalIWO(epoch=100, pop_size=50, seed_min=3, seed_max=9, exponent=3, sigma_start=0.6, sigma_end=0.01) model = SBO.DevSBO(epoch=100, pop_size=50, alpha=0.9, p_m=0.05, psw=0.02) model = SBO.OriginalSBO(epoch=100, pop_size=50, alpha=0.9, p_m=0.05, psw=0.02) model = SMA.OriginalSMA(epoch=100, pop_size=50, p_t=0.03) model = SMA.DevSMA(epoch=100, pop_size=50, p_t=0.03) model = SOA.OriginalSOA(epoch=100, pop_size=50, fc=2) model = SOA.DevSOA(epoch=100, pop_size=50, fc=2) model = SOS.OriginalSOS(epoch=100, pop_size=50) model = TPO.DevTPO(epoch=100, pop_size=50, alpha=0.3, beta=50., theta=0.9) model = TSA.OriginalTSA(epoch=100, pop_size=50) model = VCS.OriginalVCS(epoch=100, pop_size=50, lamda=0.5, sigma=0.3) model = VCS.DevVCS(epoch=100, pop_size=50, lamda=0.5, sigma=0.3) model = WHO.OriginalWHO(epoch=100, pop_size=50, n_explore_step=3, n_exploit_step=3, eta=0.15, p_hi=0.9, local_alpha=0.9, local_beta=0.3, global_alpha=0.2, global_beta=0.8, delta_w=2.0, delta_c=2.0) model = AOA.OriginalAOA(epoch=100, pop_size=50, alpha=5, miu=0.5, moa_min=0.2, moa_max=0.9) model = CEM.OriginalCEM(epoch=100, pop_size=50, n_best=20, alpha=0.7) model = CGO.OriginalCGO(epoch=100, pop_size=50) model = CircleSA.OriginalCircleSA(epoch=100, pop_size=50, c_factor=0.8) model = GBO.OriginalGBO(epoch=100, pop_size=50, pr=0.5, beta_min=0.2, beta_max=1.2) model = HC.OriginalHC(epoch=100, pop_size=50, neighbour_size=50) model = HC.SwarmHC(epoch=100, pop_size=50, neighbour_size=10) model = INFO.OriginalINFO(epoch=100, pop_size=50) model = PSS.OriginalPSS(epoch=100, pop_size=50, acceptance_rate=0.8, sampling_method="LHS") model = RUN.OriginalRUN(epoch=100, pop_size=50) model = SCA.OriginalSCA(epoch=100, pop_size=50) model = SCA.DevSCA(epoch=100, pop_size=50) model = SCA.QleSCA(epoch=100, pop_size=50, alpha=0.1, gama=0.9) model = SHIO.OriginalSHIO(epoch=100, pop_size=50) model = TS.OriginalTS(epoch=100, pop_size=50, tabu_size=5, neighbour_size=20, perturbation_scale=0.05) model = HS.OriginalHS(epoch=100, pop_size=50, c_r=0.95, pa_r=0.05) model = HS.DevHS(epoch=100, pop_size=50, c_r=0.95, pa_r=0.05) model = AEO.OriginalAEO(epoch=100, pop_size=50) model = AEO.EnhancedAEO(epoch=100, pop_size=50) model = AEO.ModifiedAEO(epoch=100, pop_size=50) model = AEO.ImprovedAEO(epoch=100, pop_size=50) model = AEO.AugmentedAEO(epoch=100, pop_size=50) model = GCO.OriginalGCO(epoch=100, pop_size=50, cr=0.7, wf=1.25) model = GCO.DevGCO(epoch=100, pop_size=50, cr=0.7, wf=1.25) model = WCA.OriginalWCA(epoch=100, pop_size=50, nsr=4, wc=2.0, dmax=1e-6) model = CRO.OriginalCRO(epoch=100, pop_size=50, po=0.4, Fb=0.9, Fa=0.1, Fd=0.1, Pd=0.5, GCR=0.1, gamma_min=0.02, gamma_max=0.2, n_trials=5) model = CRO.OCRO(epoch=100, pop_size=50, po=0.4, Fb=0.9, Fa=0.1, Fd=0.1, Pd=0.5, GCR=0.1, gamma_min=0.02, gamma_max=0.2, n_trials=5, restart_count=50) model = DE.OriginalDE(epoch=100, pop_size=50, wf=0.7, cr=0.9, strategy=0) model = DE.JADE(epoch=100, pop_size=50, miu_f=0.5, miu_cr=0.5, pt=0.1, ap=0.1) model = DE.SADE(epoch=100, pop_size=50) model = DE.SAP_DE(epoch=100, pop_size=50, branch="ABS") model = EP.OriginalEP(epoch=100, pop_size=50, bout_size=0.05) model = EP.LevyEP(epoch=100, pop_size=50, bout_size=0.05) model = ES.OriginalES(epoch=100, pop_size=50, lamda=0.75) model = ES.LevyES(epoch=100, pop_size=50, lamda=0.75) model = ES.CMA_ES(epoch=100, pop_size=50) model = ES.Simple_CMA_ES(epoch=100, pop_size=50) model = FPA.OriginalFPA(epoch=100, pop_size=50, p_s=0.8, levy_multiplier=0.2) model = MA.OriginalMA(epoch=100, pop_size=50, pc=0.85, pm=0.15, p_local=0.5, max_local_gens=10, bits_per_param=4) model = SHADE.OriginalSHADE(epoch=100, pop_size=50, miu_f=0.5, miu_cr=0.5) model = SHADE.L_SHADE(epoch=100, pop_size=50, miu_f=0.5, miu_cr=0.5) model = BRO.OriginalBRO(epoch=100, pop_size=50, threshold=3) model = BRO.DevBRO(epoch=100, pop_size=50, threshold=3) model = BSO.OriginalBSO(epoch=100, pop_size=50, m_clusters=5, p1=0.2, p2=0.8, p3=0.4, p4=0.5, slope=20) model = BSO.ImprovedBSO(epoch=100, pop_size=50, m_clusters=5, p1=0.25, p2=0.5, p3=0.75, p4=0.6) model = CA.OriginalCA(epoch=100, pop_size=50, accepted_rate=0.15) model = CHIO.OriginalCHIO(epoch=100, pop_size=50, brr=0.15, max_age=10) model = CHIO.DevCHIO(epoch=100, pop_size=50, brr=0.15, max_age=10) model = FBIO.OriginalFBIO(epoch=100, pop_size=50) model = FBIO.DevFBIO(epoch=100, pop_size=50) model = GSKA.OriginalGSKA(epoch=100, pop_size=50, pb=0.1, kf=0.5, kr=0.9, kg=5) model = GSKA.DevGSKA(epoch=100, pop_size=50, pb=0.1, kr=0.9) model = HBO.OriginalHBO(epoch=100, pop_size=50, degree=3) model = HCO.OriginalHCO(epoch=100, pop_size=50, wfp=0.65, wfv=0.05, c1=1.4, c2=1.4) model = ICA.OriginalICA(epoch=100, pop_size=50, empire_count=5, assimilation_coeff=1.5, revolution_prob=0.05, revolution_rate=0.1, revolution_step_size=0.1, zeta=0.1) model = LCO.OriginalLCO(epoch=100, pop_size=50, r1=2.35) model = LCO.ImprovedLCO(epoch=100, pop_size=50) model = LCO.DevLCO(epoch=100, pop_size=50, r1=2.35) model = WarSO.OriginalWarSO(epoch=100, pop_size=50, rr=0.1) model = TOA.OriginalTOA(epoch=100, pop_size=50) model = TLO.OriginalTLO(epoch=100, pop_size=50) model = TLO.ImprovedTLO(epoch=100, pop_size=50, n_teachers=5) model = TLO.DevTLO(epoch=100, pop_size=50) model = SSDO.OriginalSSDO(epoch=100, pop_size=50) model = SPBO.OriginalSPBO(epoch=100, pop_size=50) model = SPBO.DevSPBO(epoch=100, pop_size=50) model = SARO.OriginalSARO(epoch=100, pop_size=50, se=0.5, mu=50) model = SARO.DevSARO(epoch=100, pop_size=50, se=0.5, mu=50) model = QSA.OriginalQSA(epoch=100, pop_size=50) model = QSA.DevQSA(epoch=100, pop_size=50) model = QSA.OppoQSA(epoch=100, pop_size=50) model = QSA.LevyQSA(epoch=100, pop_size=50) model = QSA.ImprovedQSA(epoch=100, pop_size=50) model = ArchOA.OriginalArchOA(epoch=100, pop_size=50, c1=2, c2=5, c3=2, c4=0.5, acc_max=0.9, acc_min=0.1) model = ASO.OriginalASO(epoch=100, pop_size=50, alpha=50, beta=0.2) model = CDO.OriginalCDO(epoch=100, pop_size=50) model = EFO.OriginalEFO(epoch=100, pop_size=50, r_rate=0.3, ps_rate=0.85, p_field=0.1, n_field=0.45) model = EFO.DevEFO(epoch=100, pop_size=50, r_rate=0.3, ps_rate=0.85, p_field=0.1, n_field=0.45) model = EO.OriginalEO(epoch=100, pop_size=50) model = EO.AdaptiveEO(epoch=100, pop_size=50) model = EO.ModifiedEO(epoch=100, pop_size=50) model = EVO.OriginalEVO(epoch=100, pop_size=50) model = FLA.OriginalFLA(epoch=100, pop_size=50, C1=0.5, C2=2.0, C3=0.1, C4=0.2, C5=2.0, DD=0.01) model = HGSO.OriginalHGSO(epoch=100, pop_size=50, n_clusters=3) model = MVO.OriginalMVO(epoch=100, pop_size=50, wep_min=0.2, wep_max=1.0) model = MVO.DevMVO(epoch=100, pop_size=50, wep_min=0.2, wep_max=1.0) model = NRO.OriginalNRO(epoch=100, pop_size=50) model = RIME.OriginalRIME(epoch=100, pop_size=50, sr=5.0) model = SA.OriginalSA(epoch=100, pop_size=50, temp_init=100, step_size=0.1) model = SA.GaussianSA(epoch=100, pop_size=50, temp_init=100, cooling_rate=0.99, scale=0.1) model = SA.SwarmSA(epoch=100, pop_size=50, max_sub_iter=5, t0=1000, t1=1, move_count=5, mutation_rate=0.1, mutation_step_size=0.1, mutation_step_size_damp=0.99) model = WDO.OriginalWDO(epoch=100, pop_size=50, RT=3, g_c=0.2, alp=0.4, c_e=0.4, max_v=0.3) model = TWO.OriginalTWO(epoch=100, pop_size=50) model = TWO.EnhancedTWO(epoch=100, pop_size=50) model = TWO.OppoTWO(epoch=100, pop_size=50) model = TWO.LevyTWO(epoch=100, pop_size=50) model = ABC.OriginalABC(epoch=100, pop_size=50, n_limits=50) model = ACOR.OriginalACOR(epoch=100, pop_size=50, sample_count=25, intent_factor=0.5, zeta=1.0) model = AGTO.OriginalAGTO(epoch=100, pop_size=50, p1=0.03, p2=0.8, beta=3.0) model = AGTO.MGTO(epoch=100, pop_size=50, pp=0.03) model = BeesA.OriginalBeesA(epoch=100, pop_size=50, selected_site_ratio=0.5, elite_site_ratio=0.4, selected_site_bee_ratio=0.1, elite_site_bee_ratio=2.0, dance_radius=0.1, dance_reduction=0.99) model = BeesA.CleverBookBeesA(epoch=100, pop_size=50, n_elites=16, n_others=4, patch_size=5.0, patch_reduction=0.985, n_sites=3, n_elite_sites=1) model = BeesA.ProbBeesA(epoch=100, pop_size=50, recruited_bee_ratio=0.1, dance_radius=0.1, dance_reduction=0.99) model = BES.OriginalBES(epoch=100, pop_size=50, a_factor=10, R_factor=1.5, alpha=2.0, c1=2.0, c2=2.0) model = BFO.OriginalBFO(epoch=100, pop_size=50, Ci=0.01, Ped=0.25, Nc=5, Ns=4, d_attract=0.1, w_attract=0.2, h_repels=0.1, w_repels=10) model = BFO.ABFO(epoch=100, pop_size=50, C_s=0.1, C_e=0.001, Ped=0.01, Ns=4, N_adapt=2, N_split=40) model = ZOA.OriginalZOA(epoch=100, pop_size=50) model = WOA.OriginalWOA(epoch=100, pop_size=50) model = WOA.HI_WOA(epoch=100, pop_size=50, feedback_max=10) model = WaOA.OriginalWaOA(epoch=100, pop_size=50) model = TSO.OriginalTSO(epoch=100, pop_size=50) model = TDO.OriginalTDO(epoch=100, pop_size=50) model = STO.OriginalSTO(epoch=100, pop_size=50) model = SSpiderO.OriginalSSpiderO(epoch=100, pop_size=50, fp_min=0.65, fp_max=0.9) model = SSpiderA.OriginalSSpiderA(epoch=100, pop_size=50, r_a=1.0, p_c=0.7, p_m=0.1) model = SSO.OriginalSSO(epoch=100, pop_size=50) model = SSA.OriginalSSA(epoch=100, pop_size=50, ST=0.8, PD=0.2, SD=0.1) model = SSA.DevSSA(epoch=100, pop_size=50, ST=0.8, PD=0.2, SD=0.1) model = SRSR.OriginalSRSR(epoch=100, pop_size=50) model = SLO.OriginalSLO(epoch=100, pop_size=50) model = SLO.ModifiedSLO(epoch=100, pop_size=50) model = SLO.ImprovedSLO(epoch=100, pop_size=50, c1=1.2, c2=1.5) model = SHO.OriginalSHO(epoch=100, pop_size=50, h_factor=5.0, n_trials=10) model = SFO.OriginalSFO(epoch=100, pop_size=50, pp=0.1, AP=4.0, epsilon=0.0001) model = SFO.ImprovedSFO(epoch=100, pop_size=50, pp=0.1) model = ServalOA.OriginalServalOA(epoch=100, pop_size=50) model = SeaHO.OriginalSeaHO(epoch=100, pop_size=50) model = SCSO.OriginalSCSO(epoch=100, pop_size=50) model = POA.OriginalPOA(epoch=100, pop_size=50) model = PFA.OriginalPFA(epoch=100, pop_size=50) model = OOA.OriginalOOA(epoch=100, pop_size=50) model = NGO.OriginalNGO(epoch=100, pop_size=50) model = NMRA.OriginalNMRA(epoch=100, pop_size=50, pb=0.75) model = NMRA.ImprovedNMRA(epoch=100, pop_size=50, pb=0.75, pm=0.01) model = MSA.OriginalMSA(epoch=100, pop_size=50, n_best=5, partition=0.5, max_step_size=1.0) model = MRFO.OriginalMRFO(epoch=100, pop_size=50, somersault_range=2.0) model = MRFO.WMQIMRFO(epoch=100, pop_size=50, somersault_range=2.0, pm=0.5) model = MPA.OriginalMPA(epoch=100, pop_size=50) model = MGO.OriginalMGO(epoch=100, pop_size=50) model = MFO.OriginalMFO(epoch=100, pop_size=50) model = JA.OriginalJA(epoch=100, pop_size=50) model = JA.LevyJA(epoch=100, pop_size=50) model = JA.DevJA(epoch=100, pop_size=50) model = HHO.OriginalHHO(epoch=100, pop_size=50) model = HGS.OriginalHGS(epoch=100, pop_size=50, PUP=0.08, LH=10000) model = HBA.OriginalHBA(epoch=100, pop_size=50) model = GWO.OriginalGWO(epoch=100, pop_size=50) model = GWO.RW_GWO(epoch=100, pop_size=50) model = GTO.OriginalGTO(epoch=100, pop_size=50, A=0.4, H=2.0) model = GTO.Matlab101GTO(epoch=100, pop_size=50) model = GTO.Matlab102GTO(epoch=100, pop_size=50) model = GOA.OriginalGOA(epoch=100, pop_size=50, c_min=0.00004, c_max=1.0) model = GJO.OriginalGJO(epoch=100, pop_size=50) model = FOX.OriginalFOX(epoch=100, pop_size=50, c1=0.18, c2=0.82) model = FOA.OriginalFOA(epoch=100, pop_size=50) model = FOA.WhaleFOA(epoch=100, pop_size=50) model = FOA.DevFOA(epoch=100, pop_size=50) model = FFO.OriginalFFO(epoch=100, pop_size=50) model = FFA.OriginalFFA(epoch=100, pop_size=50, gamma=0.001, beta_base=2, alpha=0.2, alpha_damp=0.99, delta=0.05, exponent=2) model = FA.OriginalFA(epoch=100, pop_size=50, max_sparks=50, p_a=0.04, p_b=0.8, max_ea=40, m_sparks=50) model = ESOA.OriginalESOA(epoch=100, pop_size=50) model = EHO.OriginalEHO(epoch=100, pop_size=50, alpha=0.5, beta=0.5, n_clans=5) model = DO.OriginalDO(epoch=100, pop_size=50) model = DMOA.OriginalDMOA(epoch=100, pop_size=50, n_baby_sitter=3, peep=2) model = DMOA.DevDMOA(epoch=100, pop_size=50, peep=2) model = CSO.OriginalCSO(epoch=100, pop_size=50, mixture_ratio=0.15, smp=5, spc=False, cdc=0.8, srd=0.15, c1=0.4, w_min=0.4, w_max=0.9) model = CSA.OriginalCSA(epoch=100, pop_size=50, p_a=0.3) model = CoatiOA.OriginalCoatiOA(epoch=100, pop_size=50) model = COA.OriginalCOA(epoch=100, pop_size=50, n_coyotes=5) model = BSA.OriginalBSA(epoch=100, pop_size=50, ff=10, pff=0.8, c1=1.5, c2=1.5, a1=1.0, a2=1.0, fc=0.5) ## Newly added algorithms in version 3.0.3 model = GWO.GWO_WOA(epoch=100, pop_size=50) model = GWO.IGWO(epoch=1000, pop_size=50, a_min = 0.02, a_max = 2.2) model = GWO.ChaoticGWO(epoch=1000, pop_size=50, chaotic_name="chebyshev", initial_chaotic_value=0.7) model = GWO.FuzzyGWO(epoch=1000, pop_size=50, fuzzy_name="increase") model = GWO.IncrementalGWO(epoch=1000, pop_size=50, explore_factor=1.5) model = GWO.ExGWO(epoch=1000, pop_size=50) model = GWO.DS_GWO(epoch=1000, pop_size=50, explore_ratio=0.4, n_groups=5) model = GWO.IOBL_GWO(epoch=1000, pop_size=50) model = GWO.OGWO(epoch=1000, pop_size=50, miu_factor=2.0, jumping_rate=0.05) model = GWO.ER_GWO(epoch=1000, pop_size=50, a_initial=2.0, a_final=0.0, miu_factor=1.0001) model = GWO.CG_GWO(epoch=1000, pop_size=50) model = ESO.OriginalESO(epoch=1000, pop_size=50) model = AO.AAO(epoch=1000, pop_size=50, sharpness=10.0, sigmoid_midpoint=0.5) model = EPC.DevEPC(epoch=1000, pop_size=50, heat_damping_factor=0.95, mutation_factor=0.1, spiral_a=1.0, spiral_b=0.5) model = SMO.DevSMO(epoch=1000, pop_size=50, max_groups = 5, perturbation_rate = 0.7) model = SquirrelSA.OriginalSquirrelSA(epoch=1000, pop_size=50, n_food_sources=4, predator_prob=0.1, gliding_constant=1.9, scaling_factor=18, beta=1.5) model = AFT.OriginalAFT(epoch=1000, pop_size=50) model = CDDO.OriginalCDDO(epoch=1000, pop_size=50, pattern_size=10, creativity_rate=0.1) model = FDO.OriginalFDO(epoch=1000, pop_size=50, weight_factor=0.1) model = LSHADEcnEpSin.OriginalLSHADEcnEpSin(epoch=1000, pop_size=50, miu_f = 0.5, miu_cr = 0.5, freq = 0.5, memory_size = 5, ps = 0.5, pc = 0.4, pop_size_min = 10) model = IMODE.OriginalIMODE(epoch=1000, pop_size=50, memory_size=5, archive_size=20) ```

## ✅ Examples ### Simple Benchmark Function MEALPY allows you to define your optimization problem in a couple of ways. #### 1. Define Problem as a Dictionary You can quickly define your problem using a Python dictionary. However, this approach is only valid for problems with float decision variables. ```python from mealpy import FloatVar, SMA import numpy as np def objective_function(solution): return np.sum(solution**2) problem = { "obj_func": objective_function, "bounds": FloatVar(lb=(-100., )*30, ub=(100., )*30), "minmax": "min", "log_to": "console", } ## Run the algorithm model = SMA.OriginalSMA(epoch=100, pop_size=50, pr=0.03) g_best = model.solve(problem) print(f"Best solution: {g_best.solution}, Best fitness: {g_best.target.fitness}") ``` #### 2. Define a Custom Problem Class For more complex scenarios, especially when your decision variables are not exclusively `FloatVar`, **we recommend defining a custom class that inherits from the Problem class.** Let's demonstrate this with a simple "Squared" class. In the `__init__` method of your custom Problem class (e.g., Squared class), you must set the bounds and minmax attributes of the problem. + `bounds`: Defines the search space and the type of decision variables (e.g., `FloatVar`, `IntegerVar`). + `minmax`: A string indicating whether the problem is a minimization ("min") or maximization ("max") problem. After defining the initialization, you must override the abstract method `obj_func()`. This method is the core of your problem definition: + It takes a single parameter: solution (the encoded solution vector generated by the optimizer). + It must return the objective function value (or fitness) for the given solution. The resulting code structure for a custom problem class would look similar to the snippet below. You can include any additional parameters you need in your custom class (like '`data`' or '`name`' in this example). ```python from mealpy import Problem, FloatVar, BBO import numpy as np # Our custom problem class class Squared(Problem): def __init__(self, bounds=None, minmax="min", data=None, **kwargs): super().__init__(bounds, minmax, **kwargs) self.data = data # This is additional variable use for passing data to objective function def obj_func(self, solution): return np.sum(solution ** 2) ## Now, we define an algorithm, and pass an instance of our *Squared* class as the problem argument. bound = FloatVar(lb=(-10., )*20, ub=(10., )*20, name="my_var") # The `name` of variable is important when decoding. problem = Squared(bounds=bound, minmax="min", name="Squared", data="Amazing") model = BBO.OriginalBBO(epoch=100, pop_size=20) g_best = model.solve(problem) ## Show some attributes print(g_best.solution) print(g_best.target.fitness) print(g_best.target.objectives) print(g_best) print(model.get_parameters()) print(model.get_name()) print(model.get_attributes()["g_best"]) print(model.problem.get_name()) print(model.problem.n_dims) print(model.problem.bounds) print(model.problem.lb) print(model.problem.ub) ``` We provide many examples for complicated applications that can use Mealpy to solve. ## 🚀 Mealpy Applications MEALPY is a versatile library capable of solving a wide array of complex optimization problems across various domains. Below are examples showcasing its diverse applications. ### 1. General Optimization Problems These examples demonstrate MEALPY's use in common optimization scenarios. 1. Large-Scale Optimization [example](/examples/applications/run_large_scale_optimization.py) 2. Distributed Optimization / Parallelization Optimization [example](/examples/applications/run_distributed_optimization.py) 3. Constrained Benchmark Function [example](/examples/applications/run_constraint_functions.py) 4. Multi-objective Benchmark Function [example](/examples/applications/run_multi_objective_functions.py) ### 2. Machine Learning & AI Optimization MEALPY can be effectively used to optimize various aspects of Machine Learning and AI models. 1. Optimize Machine Learning Model (SVM) Hyperparameters [example](/examples/applications/sklearn/svm_hyperparameter_optimization.py) 2. Optimize Linear Regression Model with Pytorch: [example](/examples/applications/pytorch/linear_regression.py) ### 3. Combinatorial Optimization Problems MEALPY excels at solving complex combinatorial problems, which involve finding an optimal object from a finite set of objects. 1. Traveling Salesman Problem (TSP) [example](/examples/applications/discrete-problems/traveling_salesman_problem.py) 2. Job Shop Scheduling Problem [example](/examples/applications/discrete-problems/job_shop_scheduling.py) 3. Shortest Path Problem [example](/examples/applications/discrete-problems/shortest_path_problem.py) 4. Location Optimization [example](/examples/applications/discrete-problems/location_optimization.py) 5. Supply Chain Optimization [example](/examples/applications/discrete-problems/supply_chain_optimization.py) 6. Healthcare Workflow Optimization Problem [example](/examples/applications/discrete-problems/workflow_optimization.py) 7. Production Optimization Problem [example](/examples/applications/discrete-problems/production_optimization.py) 8. Employee Rostering Problem [example](/examples/applications/discrete-problems/employee_rostering.py) 9. Maintenance Scheduling [example](/examples/applications/discrete-problems/maintenance_scheduling.py) 10. Cloud task scheduling [example](/examples/applications/discrete-problems/cloud_task_scheduling.py) ### 4. Advanced Integration Examples MEALPY's flexibility allows for integration into more specialized systems and workflows. #### MEALPY + Neural Networks (Replacing Gradient Descent) * Time-series Problem: * Traditional MLP [Link](/examples/applications/keras/traditional-mlp-time-series.py) * Hybrid code (Mealpy + MLP): [Link](/examples/applications/keras/mha-hybrid-mlp-time-series.py) * Classification Problem: * Traditional MLP [Link](/examples/applications/keras/traditional-mlp-classification.py) * Hybrid code (Mealpy + MLP): [Link](/examples/applications/keras/mha-hybrid-mlp-classification.py) #### MEALPY + Neural Network (Optimize Neural Network Hyper-parameter) Code: [Link](https://github.com/thieu1995/mealpy/blob/master/examples/applications/keras/mha-hyper-parameter-mlp-time-series.py) ### 5. Dedicated Utility Classes MEALPY includes specialized classes to streamline common optimization tasks. 1. Tuner class (GridSearchCV/ParameterSearch, Hyper-parameter tuning) [example](/examples/run_tuner.py) 2. Multitask class (Multitask solver) [example](/examples/run_multitask.py) 3. Visualization [Tutorials](/examples/utils/visualize/all_charts.py) ### 6. External Projects & More Examples Explore additional advanced examples and dedicated projects showcasing MEALPY's capabilities. * Travelling Salesman Problem: [link](https://github.com/thieu1995/MHA-TSP) * Feature selection problem: [link](https://github.com/thieu1995/MHA-FS) For more usage examples please look at [examples](/examples) folder. More advanced examples can also be found in the [Mealpy-examples repository](https://github.com/thieu1995/mealpy_examples). ### 7. Tutorial Videos & Resources All tutorial videos: [Link](https://mealpy.readthedocs.io/en/latest/pages/general/video_tutorials.html) All code examples: [Link](/examples) All visualization examples: [Link](https://mealpy.readthedocs.io/en/latest/pages/visualization.html) # 📚 Documents ## 📎 Official channels * 🔗 [Official source code repository](https://github.com/thieu1995/mealpy) * 📘 [Official document](https://mealpy.readthedocs.io/) * 📦 [Download releases](https://pypi.org/project/mealpy/) * 🐞 [Issue tracker](https://github.com/thieu1995/mealpy/issues) * 📝 [Notable changes log](/ChangeLog.md) * 📝 [Examples with different meapy version](/examples/EXAMPLES.md) * 💬 [Official discussion group](https://t.me/+fRVCJGuGJg1mNDg1) ## 🌟 MEALPY ecosystem * [Mealpy + Multi-Layer Perceptron](https://github.com/thieu1995/MetaPerceptron) * [Mealpy + Extreme Learning Machine](https://github.com/thieu1995/IntelELM) * [Mealpy + Random Vector Functional Link Neural Network](https://github.com/thieu1995/GrafoRVFL) * [Mealpy + KMeans clustering](https://github.com/thieu1995/MetaCluster) * [Mealpy + Cascade-Forward Neural Network](https://github.com/thieu1995/deforce) * [Mealpy + Higher Order Functional Link Neural Network](https://github.com/thieu1995/reflame) * [Mealpy + Radial Basis Function](https://github.com/thieu1995/EvoRBF) * [Mealpy + Adaptive Neuro Fuzzy Inference System](https://github.com/thieu1995/X-ANFIS) * [Mealpy + Wavelet Neural Network](https://github.com/thieu1995/WaveletML) * [Mealpy + Kolmogorov–Arnold Network](https://github.com/thieu1995/MetaKan) * [Mealpy + Feature Selection](https://github.com/thieu1995/mafese) * [Mealpy + Scikit-Learn](https://github.com/thieu1995/MetaSklearn) * [Mealpy + Immune Algorithm-Inspired Neural Network](https://github.com/thieu1995/IMAINET)

References

### A * **ABC - Artificial Bee Colony** * **OriginalABC**: Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization (Vol. 200, pp. 1-10). Technical report-tr06, Erciyes university, engineering faculty, computer engineering department. * **ACOR - Ant Colony Optimization**. * **OriginalACOR**: Socha, K., & Dorigo, M. (2008). Ant colony optimization for continuous domains. European journal of operational research, 185(3), 1155-1173. * **ALO - Ant Lion Optimizer** * **OriginalALO**: Mirjalili S (2015). “The Ant Lion Optimizer.” Advances in Engineering Software, 83, 80-98. doi: [10.1016/j.advengsoft.2015.01.010](https://doi.org/10.1016/j.advengsoft.2015.01.010) * **BaseALO**: The developed version * **AEO - Artificial Ecosystem-based Optimization** * **OriginalAEO**: Zhao, W., Wang, L., & Zhang, Z. (2019). Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm. Neural Computing and Applications, 1-43. * **AugmentedAEO**: Van Thieu, N., Barma, S. D., Van Lam, T., Kisi, O., & Mahesha, A. (2022). Groundwater level modeling using Augmented Artificial Ecosystem Optimization. Journal of Hydrology, 129034. * **ImprovedAEO**: Rizk-Allah, R. M., & El-Fergany, A. A. (2020). Artificial ecosystem optimizer for parameters identification of proton exchange membrane fuel cells model. International Journal of Hydrogen Energy. * **EnhancedAEO**: Eid, A., Kamel, S., Korashy, A., & Khurshaid, T. (2020). An Enhanced Artificial Ecosystem-Based Optimization for Optimal Allocation of Multiple Distributed Generations. IEEE Access, 8, 178493-178513. * **ModifiedAEO**: Menesy, A. S., Sultan, H. M., Korashy, A., Banakhr, F. A., Ashmawy, M. G., & Kamel, S. (2020). Effective parameter extraction of different polymer electrolyte membrane fuel cell stack models using a modified artificial ecosystem optimization algorithm. IEEE Access, 8, 31892-31909. * **ASO - Atom Search Optimization** * **OriginalASO**: Zhao, W., Wang, L., & Zhang, Z. (2019). Atom search optimization and its application to solve a hydrogeologic parameter estimation problem. Knowledge-Based Systems, 163, 283-304. * **ArchOA - Archimedes Optimization Algorithm** * **OriginalArchOA**: Hashim, F. A., Hussain, K., Houssein, E. H., Mabrouk, M. S., & Al-Atabany, W. (2021). Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Applied Intelligence, 51(3), 1531-1551. * **AOA - Arithmetic Optimization Algorithm** * **OriginalAOA**: Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A. H. (2021). The arithmetic optimization algorithm. Computer methods in applied mechanics and engineering, 376, 113609. * **AO - Aquila Optimizer** * **OriginalAO**: Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A., & Gandomi, A. H. (2021). Aquila Optimizer: A novel meta-heuristic optimization Algorithm. Computers & Industrial Engineering, 157, 107250. * **AAO**: Al-Selwi, S. M., Hassan, M. F., Abdulkadir, S. J., Ragab, M. G., Alqushaibi, A., & Sumiea, E. H. (2024). Smart grid stability prediction using adaptive aquila optimizer and ensemble stacked bilstm. Results in Engineering, 24, 103261. * **AVOA - African Vultures Optimization Algorithm** * **OriginalAVOA**: Abdollahzadeh, B., Gharehchopogh, F. S., & Mirjalili, S. (2021). African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Computers & Industrial Engineering, 158, 107408. * **AGTO - Artificial Gorilla Troops Optimization** * **OriginalAGTO**: Abdollahzadeh, B., Soleimanian Gharehchopogh, F., & Mirjalili, S. (2021). Artificial gorilla troops optimizer: a new nature‐inspired metaheuristic algorithm for global optimization problems. International Journal of Intelligent Systems, 36(10), 5887-5958. * **ARO - Artificial Rabbits Optimization**: * **OriginalARO**: Wang, L., Cao, Q., Zhang, Z., Mirjalili, S., & Zhao, W. (2022). Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 114, 105082. * **AFT - Ali baba and the Forty Thieves**: * **OriginalAFT**: Braik, M., Ryalat, M. H., & Al-Zoubi, H. (2022). A novel meta-heuristic algorithm for solving numerical optimization problems: Ali Baba and the forty thieves. Neural Computing and Applications, 34(1), 409-455. ### B * **BFO - Bacterial Foraging Optimization** * **OriginalBFO**: Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE control systems magazine, 22(3), 52-67. * **ABFO**: Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019, April). Building resource auto-scaler with functional-link neural network and adaptive bacterial foraging optimization. In International Conference on Theory and Applications of Models of Computation (pp. 501-517). Springer, Cham. * **BeesA - Bees Algorithm** * **OriginalBeesA**: Pham, D. T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., & Zaidi, M. (2005). The bees algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK. * **ProbBeesA**: The probabilitic version of: Pham, D. T., Ghanbarzadeh, A., Koç, E., Otri, S., Rahim, S., & Zaidi, M. (2006). The bees algorithm—a novel tool for complex optimisation problems. In Intelligent production machines and systems (pp. 454-459). Elsevier Science Ltd. * **BBO - Biogeography-Based Optimization** * **OriginalBBO**: Simon, D. (2008). Biogeography-based optimization. IEEE transactions on evolutionary computation, 12(6), 702-713. * **BaseBBO**: The developed version * **BA - Bat Algorithm** * **OriginalBA**: Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. In Nature inspired cooperative strategies for optimization (NICSO 2010) (pp. 65-74). Springer, Berlin, Heidelberg. * **AdaptiveBA**: Wang, X., Wang, W. and Wang, Y., 2013, July. An adaptive bat algorithm. In International Conference on Intelligent Computing(pp. 216-223). Springer, Berlin, Heidelberg. * **ModifiedBA**: Dong, H., Li, T., Ding, R. and Sun, J., 2018. A novel hybrid genetic algorithm with granular information for feature selection and optimization. Applied Soft Computing, 65, pp.33-46. * **BSO - Brain Storm Optimization** * **OriginalBSO**: . Shi, Y. (2011, June). Brain storm optimization algorithm. In International conference in swarm intelligence (pp. 303-309). Springer, Berlin, Heidelberg. * **ImprovedBSO**: El-Abd, M., 2017. Global-best brain storm optimization algorithm. Swarm and evolutionary computation, 37, pp.27-44. * **BSA - Bird Swarm Algorithm** * **OriginalBSA**: Meng, X. B., Gao, X. Z., Lu, L., Liu, Y., & Zhang, H. (2016). A new bio-inspired optimisation algorithm:Bird Swarm Algorithm. Journal of Experimental & Theoretical Artificial Intelligence, 28(4), 673-687. * **BMO - Barnacles Mating Optimizer**: * **OriginalBMO**: Sulaiman, M. H., Mustaffa, Z., Saari, M. M., Daniyal, H., Daud, M. R., Razali, S., & Mohamed, A. I. (2018, June). Barnacles mating optimizer: a bio-inspired algorithm for solving optimization problems. In 2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) (pp. 265-270). IEEE. * **BES - Bald Eagle Search** * **OriginalBES**: Alsattar, H. A., Zaidan, A. A., & Zaidan, B. B. (2019). Novel meta-heuristic bald eagle search optimisation algorithm. Artificial Intelligence Review, 1-28. * **BRO - Battle Royale Optimization** * **OriginalBRO**: Rahkar Farshi, T. (2020). Battle royale optimization algorithm. Neural Computing and Applications, 1-19. * **BaseBRO**: The developed version ### C * **CA - Culture Algorithm** * **OriginalCA**: Reynolds, R.G., 1994, February. An introduction to cultural algorithms. In Proceedings of the third annual conference on evolutionary programming (Vol. 24, pp. 131-139). River Edge, NJ: World Scientific. * **CEM - Cross Entropy Method** * **OriginalCEM**: Rubinstein, R. (1999). The cross-entropy method for combinatorial and continuous optimization. Methodology and computing in applied probability, 1(2), 127-190. * **CSO - Cat Swarm Optimization** * **OriginalCSO**: Chu, S. C., Tsai, P. W., & Pan, J. S. (2006, August). Cat swarm optimization. In Pacific Rim international conference on artificial intelligence (pp. 854-858). Springer, Berlin, Heidelberg. * **CSA - Cuckoo Search Algorithm** * **OriginalCSA**: Yang, X. S., & Deb, S. (2009, December). Cuckoo search via Lévy flights. In 2009 World congress on nature & biologically inspired computing (NaBIC) (pp. 210-214). Ieee. * **CRO - Coral Reefs Optimization** * **OriginalCRO**: Salcedo-Sanz, S., Del Ser, J., Landa-Torres, I., Gil-López, S., & Portilla-Figueras, J. A. (2014). The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems. The Scientific World Journal, 2014. * **OCRO**: Nguyen, T., Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019). Efficient time-series forecasting using neural network and opposition-based coral reefs optimization. International Journal of Computational Intelligence Systems, 12(2), 1144-1161. * **COA - Coyote Optimization Algorithm** * **OriginalCOA**: Pierezan, J., & Coelho, L. D. S. (2018, July). Coyote optimization algorithm: a new metaheuristic for global optimization problems. In 2018 IEEE congress on evolutionary computation (CEC) (pp. 1-8). IEEE. * **CHIO - Coronavirus Herd Immunity Optimization** * **OriginalCHIO**: Al-Betar, M. A., Alyasseri, Z. A. A., Awadallah, M. A., & Abu Doush, I. (2021). Coronavirus herd immunity optimizer (CHIO). Neural Computing and Applications, 33(10), 5011-5042. * **BaseCHIO**: The developed version * **CGO - Chaos Game Optimization** * **OriginalCGO**: Talatahari, S., & Azizi, M. (2021). Chaos Game Optimization: a novel metaheuristic algorithm. Artificial Intelligence Review, 54(2), 917-1004. * **CSA - Circle Search Algorithm** * **OriginalCSA**: Qais, M. H., Hasanien, H. M., Turky, R. A., Alghuwainem, S., Tostado-Véliz, M., & Jurado, F. (2022). Circle Search Algorithm: A Geometry-Based Metaheuristic Optimization Algorithm. Mathematics, 10(10), 1626. * **CDDO - Child Drawing Development Optimization** * **OriginalCDDO**: Abdulhameed, S., Rashid, T.A. Child Drawing Development Optimization Algorithm Based on Child’s Cognitive Development. Arab J Sci Eng 47, 1337–1351 (2022). https://doi.org/10.1007/s13369-021-05928-6 ### D * **DE - Differential Evolution** * **BaseDE**: Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), 341-359. * **JADE**: Zhang, J., & Sanderson, A. C. (2009). JADE: adaptive differential evolution with optional external archive. IEEE Transactions on evolutionary computation, 13(5), 945-958. * **SADE**: Qin, A. K., & Suganthan, P. N. (2005, September). Self-adaptive differential evolution algorithm for numerical optimization. In 2005 IEEE congress on evolutionary computation (Vol. 2, pp. 1785-1791). IEEE. * **SHADE**: Tanabe, R., & Fukunaga, A. (2013, June). Success-history based parameter adaptation for differential evolution. In 2013 IEEE congress on evolutionary computation (pp. 71-78). IEEE. * **L_SHADE**: Tanabe, R., & Fukunaga, A. S. (2014, July). Improving the search performance of SHADE using linear population size reduction. In 2014 IEEE congress on evolutionary computation (CEC) (pp. 1658-1665). IEEE. * **SAP_DE**: Teo, J. (2006). Exploring dynamic cls-adaptive populations in differential evolution. Soft Computing, 10(8), 673-686. * **DSA - Differential Search Algorithm (not done)** * **BaseDSA**: Civicioglu, P. (2012). Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Computers & Geosciences, 46, 229-247. * **DO - Dragonfly Optimization** * **OriginalDO**: Mirjalili, S. (2016). Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), 1053-1073. * **DMOA - Dwarf Mongoose Optimization Algorithm** * **OriginalDMOA**: Agushaka, J. O., Ezugwu, A. E., & Abualigah, L. (2022). Dwarf mongoose optimization algorithm. Computer methods in applied mechanics and engineering, 391, 114570. * **DevDMOA**: The developed version ### E * **ES - Evolution Strategies** . * **OriginalES**: Schwefel, H. P. (1984). Evolution strategies: A family of non-linear optimization techniques based on imitating some principles of organic evolution. Annals of Operations Research, 1(2), 165-167. * **LevyES**: Zhang, S., & Salari, E. (2005). Competitive learning vector quantization with evolution strategies for image compression. Optical Engineering, 44(2), 027006. * **EP - Evolutionary programming** . * **OriginalEP**: Fogel, L. J. (1994). Evolutionary programming in perspective: The top-down view. Computational intelligence: Imitating life. * **LevyEP**: Lee, C.Y. and Yao, X., 2001, May. Evolutionary algorithms with adaptive lévy mutations. In Proceedings of the 2001 congress on evolutionary computation (IEEE Cat. No. 01TH8546) (Vol. 1, pp. 568-575). IEEE. * **EHO - Elephant Herding Optimization** . * **OriginalEHO**: Wang, G. G., Deb, S., & Coelho, L. D. S. (2015, December). Elephant herding optimization. In 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI) (pp. 1-5). IEEE. * **EFO - Electromagnetic Field Optimization** . * **OriginalEFO**:Abedinpourshotorban, H., Shamsuddin, S. M., Beheshti, Z., & Jawawi, D. N. (2016). Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm. Swarm and Evolutionary Computation, 26, 8-22. * **BaseEFO**: The developed version * **EOA - Earthworm Optimisation Algorithm** . * **OriginalEOA**: Wang, G. G., Deb, S., & dos Santos Coelho, L. (2018). Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems. IJBIC, 12(1), 1-22. * **EO - Equilibrium Optimizer** . * **OriginalEO**: Faramarzi, A., Heidarinejad, M., Stephens, B., & Mirjalili, S. (2019). Equilibrium optimizer: A novel optimization algorithm. Knowledge-Based Systems. * **ModifiedEO**: Gupta, S., Deep, K., & Mirjalili, S. (2020). An efficient equilibrium optimizer with mutation strategy for numerical optimization. Applied Soft Computing, 96, 106542. * **AdaptiveEO**: Wunnava, A., Naik, M. K., Panda, R., Jena, B., & Abraham, A. (2020). A novel interdependence based multilevel thresholding technique using adaptive equilibrium optimizer. Engineering Applications of Artificial Intelligence, 94, 103836. * **ESO - Electrical Storm Optimization** . * **OriginalESO**: Soto Calvo, M., & Lee, H. S. (2025). Electrical Storm Optimization (ESO) Algorithm: Theoretical Foundations, Analysis, and Application to Engineering Problems. Machine Learning and Knowledge Extraction, 7(1), 24. https://doi.org/10.3390/make7010024 * **EPC - Emperor Penguins Colony** . * **DevEPC**: Harifi, S., Khalilian, M., Mohammadzadeh, J. and Ebrahimnejad, S., 2019. Emperor Penguins Colony: a new metaheuristic algorithm for optimization. Evolutionary intelligence, 12(2), pp.211-226. ### F * **FFA - Firefly Algorithm** * **OriginalFFA**: Łukasik, S., & Żak, S. (2009, October). Firefly algorithm for continuous constrained optimization tasks. In International conference on computational collective intelligence (pp. 97-106). Springer, Berlin, Heidelberg. * **FA - Fireworks algorithm** * **OriginalFA**: Tan, Y., & Zhu, Y. (2010, June). Fireworks algorithm for optimization. In International conference in swarm intelligence (pp. 355-364). Springer, Berlin, Heidelberg. * **FPA - Flower Pollination Algorithm** * **OriginalFPA**: Yang, X. S. (2012, September). Flower pollination algorithm for global optimization. In International conference on unconventional computing and natural computation (pp. 240-249). Springer, Berlin, Heidelberg. * **FOA - Fruit-fly Optimization Algorithm** * **OriginalFOA**: Pan, W. T. (2012). A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowledge-Based Systems, 26, 69-74. * **BaseFOA**: The developed version * **WhaleFOA**: Fan, Y., Wang, P., Heidari, A. A., Wang, M., Zhao, X., Chen, H., & Li, C. (2020). Boosted hunting-based fruit fly optimization and advances in real-world problems. Expert Systems with Applications, 159, 113502. * **FDO - Fitness Dependent Optimizer** * **OriginalFDO**: Abdullah, J. M., & Ahmed, T. (2019). Fitness dependent optimizer: inspired by the bee swarming reproductive process. IEEe Access, 7, 43473-43486. * **FBIO - Forensic-Based Investigation Optimization** * **OriginalFBIO**: Chou, J.S. and Nguyen, N.M., 2020. FBI inspired meta-optimization. Applied Soft Computing, p.106339. * **BaseFBIO**: Fathy, A., Rezk, H. and Alanazi, T.M., 2021. Recent approach of forensic-based investigation algorithm for optimizing fractional order PID-based MPPT with proton exchange membrane fuel cell.IEEE Access,9, pp.18974-18992. * **FHO - Fire Hawk Optimization** * **OriginalFHO**: Azizi, M., Talatahari, S., & Gandomi, A. H. (2022). Fire Hawk Optimizer: a novel metaheuristic algorithm. Artificial Intelligence Review, 1-77. ### G * **GA - Genetic Algorithm** * **BaseGA**: Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1), 66-73. * **SingleGA**: De Falco, I., Della Cioppa, A. and Tarantino, E., 2002. Mutation-based genetic algorithm: performance evaluation. Applied Soft Computing, 1(4), pp.285-299. * **MultiGA**: De Jong, K.A. and Spears, W.M., 1992. A formal analysis of the role of multi-point crossover in genetic algorithms. Annals of mathematics and Artificial intelligence, 5(1), pp.1-26. * **EliteSingleGA**: Elite version of Single-point mutation GA * **EliteMultiGA**: Elite version of Multiple-point mutation GA * **GWO - Grey Wolf Optimizer** * **OriginalGWO**: Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61. * **RW_GWO**: Gupta, S., & Deep, K. (2019). A novel random walk grey wolf optimizer. Swarm and evolutionary computation, 44, 101-112. * **GWO_WOA**: Obadina, O. O., Thaha, M. A., Althoefer, K., & Shaheed, M. H. (2022). Dynamic characterization of a master–slave robotic manipulator using a hybrid grey wolf–whale optimization algorithm. Journal of Vibration and Control, 28(15-16), 1992-2003. * **IGWO**: Kaveh, A. & Zakian, P.. (2018). Improved GWO algorithm for optimal design of truss structures. Engineering with Computers. 34. 10.1007/s00366-017-0567-1. * **ChaoticGWO**: Kohli, M., & Arora, S. (2018). Chaotic grey wolf optimization algorithm for constrained optimization problems. Journal of computational design and engineering, 5(4), 458-472. * **FuzzyGWO**: Rodríguez, Luis, Oscar Castillo, José Soria, Patricia Melin, Fevrier Valdez, Claudia I. Gonzalez, Gabriela E. Martinez, and Jesus Soto. "A fuzzy hierarchical operator in the grey wolf optimizer algorithm." Applied Soft Computing 57 (2017): 315-328. * **IncrementalGWO**: Seyyedabbasi, A., & Kiani, F. (2021). I-GWO and Ex-GWO: improved algorithms of the Grey Wolf Optimizer to solve global optimization problems. Engineering with Computers, 37(1), 509-532. * **ExGWO**: Seyyedabbasi, A., & Kiani, F. (2021). I-GWO and Ex-GWO: improved algorithms of the Grey Wolf Optimizer to solve global optimization problems. Engineering with Computers, 37(1), 509-532. * **DS_GWO**: Jiang, Jianhua, Ziying Zhao, Yutong Liu, Weihua Li, and Huan Wang. "DSGWO: An improved grey wolf optimizer with diversity enhanced strategy based on group-stage competition and balance mechanisms." Knowledge-Based Systems 250 (2022): 109100. * **IOBL_GWO**: Bansal, J. C., & Singh, S. (2021). A better exploration strategy in Grey Wolf Optimizer. Journal of Ambient Intelligence and Humanized Computing, 12(1), 1099-1118. * **OGWO**: Yu, X., Xu, W., & Li, C. (2021). Opposition-based learning grey wolf optimizer for global optimization. Knowledge-Based Systems, 226, 107139. * **ER_GWO**: Long, W., Cai, S., Jiao, J. et al. An efficient and robust grey wolf optimizer algorithm for large-scale numerical optimization. Soft Comput 24, 997–1026 (2020). * **CG_GWO**: Li, K., Li, S., Huang, Z. et al. Grey Wolf Optimization algorithm based on Cauchy-Gaussian mutation and improved search strategy. Sci Rep 12, 18961 (2022). * **GOA - Grasshopper Optimisation Algorithm** * **OriginalGOA**: Saremi, S., Mirjalili, S., & Lewis, A. (2017). Grasshopper optimisation algorithm: theory and application. Advances in Engineering Software, 105, 30-47. * **GCO - Germinal Center Optimization** * **OriginalGCO**: Villaseñor, C., Arana-Daniel, N., Alanis, A. Y., López-Franco, C., & Hernandez-Vargas, E. A. (2018). Germinal center optimization algorithm. International Journal of Computational Intelligence Systems, 12(1), 13-27. * **BaseGCO**: The developed version * **GSKA - Gaining Sharing Knowledge-based Algorithm** * **OriginalGSKA**: Mohamed, A. W., Hadi, A. A., & Mohamed, A. K. (2019). Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm. International Journal of Machine Learning and Cybernetics, 1-29. * **BaseGSKA**: Mohamed, A.W., Hadi, A.A., Mohamed, A.K. and Awad, N.H., 2020, July. Evaluating the performance of adaptive GainingSharing knowledge based algorithm on CEC 2020 benchmark problems. In 2020 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE. * **GBO - Gradient-Based Optimizer** * **OriginalGBO**: Ahmadianfar, I., Bozorg-Haddad, O., & Chu, X. (2020). Gradient-based optimizer: A new metaheuristic optimization algorithm. Information Sciences, 540, 131-159. ### H * **HC - Hill Climbing** . * **OriginalHC**: Talbi, E. G., & Muntean, T. (1993, January). Hill-climbing, simulated annealing and genetic algorithms: a comparative study and application to the mapping problem. In [1993] Proceedings of the Twenty-sixth Hawaii International Conference on System Sciences (Vol. 2, pp. 565-573). IEEE. * **SwarmHC**: The developed version based on swarm-based idea (Original is single-solution based method) * **HS - Harmony Search** . * **OriginalHS**: Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm:harmony search. simulation, 76(2), 60-68. * **BaseHS**: The developed version * **HHO - Harris Hawks Optimization** . * **OriginalHHO**: Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849-872. * **HGSO - Henry Gas Solubility Optimization** . * **OriginalHGSO**: Hashim, F. A., Houssein, E. H., Mabrouk, M. S., Al-Atabany, W., & Mirjalili, S. (2019). Henry gas solubility optimization: A novel physics-based algorithm. Future Generation Computer Systems, 101, 646-667. * **HGS - Hunger Games Search** . * **OriginalHGS**: Yang, Y., Chen, H., Heidari, A. A., & Gandomi, A. H. (2021). Hunger games search:Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Systems with Applications, 177, 114864. * **HHOA - Horse Herd Optimization Algorithm (not done)** . * **BaseHHOA**: MiarNaeimi, F., Azizyan, G., & Rashki, M. (2021). Horse herd optimization algorithm: A nature-inspired algorithm for high-dimensional optimization problems. Knowledge-Based Systems, 213, 106711. * **HBA - Honey Badger Algorithm**: * **OriginalHBA**: Hashim, F. A., Houssein, E. H., Hussain, K., Mabrouk, M. S., & Al-Atabany, W. (2022). Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems. Mathematics and Computers in Simulation, 192, 84-110. ### I * **IWO - Invasive Weed Optimization** . * **OriginalIWO**: Mehrabian, A. R., & Lucas, C. (2006). A novel numerical optimization algorithm inspired from weed colonization. Ecological informatics, 1(4), 355-366. * **ICA - Imperialist Competitive Algorithm** * **OriginalICA**: Atashpaz-Gargari, E., & Lucas, C. (2007, September). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In 2007 IEEE congress on evolutionary computation (pp. 4661-4667). Ieee. * **IMODE - Improved Multi-operator Differential Evolution Algorithm**: * **OriginalIMODE**: Sallam, K. M., Elsayed, S. M., Chakrabortty, R. K., & Ryan, M. J. (2020, July). Improved multi-operator differential evolution algorithm for solving unconstrained problems. In 2020 IEEE congress on evolutionary computation (CEC) (pp. 1-8). IEEE. * **INFO - weIghted meaN oF vectOrs**: * **OriginalINFO**: Ahmadianfar, I., Heidari, A. A., Gandomi, A. H., Chu, X., & Chen, H. (2021). RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method. Expert Systems with Applications, 181, 115079. ### J * **JA - Jaya Algorithm** * **OriginalJA**: Rao, R. (2016). Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7(1), 19-34. * **BaseJA**: The developed version * **LevyJA**: Iacca, G., dos Santos Junior, V. C., & de Melo, V. V. (2021). An improved Jaya optimization algorithm with Levy flight. Expert Systems with Applications, 165, 113902. ### K ### L * **LSHADEcnEpSin - Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood** * **OriginalLSHADEcnEpSin**: Awad, N. H., Ali, M. Z., & Suganthan, P. N. (2017, June). Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems. In 2017 IEEE congress on evolutionary computation (CEC) (pp. 372-379). IEEE. * **LCO - Life Choice-based Optimization** * **OriginalLCO**: Khatri, A., Gaba, A., Rana, K. P. S., & Kumar, V. (2019). A novel life choice-based optimizer. Soft Computing, 1-21. * **BaseLCO**: The developed version * **ImprovedLCO**: The improved version using Gaussian distribution and Mutation Mechanism ### M * **MA - Memetic Algorithm** * **OriginalMA**: Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech concurrent computation program, C3P Report, 826, 1989. * **MFO - Moth Flame Optimization** * **OriginalMFO**: Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-based systems, 89, 228-249. * **BaseMFO**: The developed version * **MVO - Multi-Verse Optimizer** * **OriginalMVO**: Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495-513. * **BaseMVO**: The developed version * **MSA - Moth Search Algorithm** * **OriginalMSA**: Wang, G. G. (2018). Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memetic Computing, 10(2), 151-164. * **MRFO - Manta Ray Foraging Optimization** * **OriginalMRFO**: Zhao, W., Zhang, Z., & Wang, L. (2020). Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications. Engineering Applications of Artificial Intelligence, 87, 103300. * **MPA - Marine Predators Algorithm**: * **OriginalMPA**: Faramarzi, A., Heidarinejad, M., Mirjalili, S., & Gandomi, A. H. (2020). Marine Predators Algorithm: A nature-inspired metaheuristic. Expert systems with applications, 152, 113377. ### N * **NRO - Nuclear Reaction Optimization** * **OriginalNRO**: Wei, Z., Huang, C., Wang, X., Han, T., & Li, Y. (2019). Nuclear Reaction Optimization: A novel and powerful physics-based algorithm for global optimization. IEEE Access. * **NMRA - Nake Mole-Rat Algorithm** * **OriginalNMRA**: Salgotra, R., & Singh, U. (2019). The naked mole-rat algorithm. Neural Computing and Applications, 31(12), 8837-8857. * **ImprovedNMRA**: Singh, P., Mittal, N., Singh, U. and Salgotra, R., 2021. Naked mole-rat algorithm with improved exploration and exploitation capabilities to determine 2D and 3D coordinates of sensor nodes in WSNs. Arabian Journal for Science and Engineering, 46(2), pp.1155-1178. ### O ### P * **PSO - Particle Swarm Optimization** * **OriginalPSO**: Eberhart, R., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science (pp. 39-43). Ieee. * **PPSO**: Ghasemi, M., Akbari, E., Rahimnejad, A., Razavi, S. E., Ghavidel, S., & Li, L. (2019). Phasor particle swarm optimization: a simple and efficient variant of PSO. Soft Computing, 23(19), 9701-9718. * **HPSO_TVAC**: Ghasemi, M., Aghaei, J., & Hadipour, M. (2017). New cls-organising hierarchical PSO with jumping time-varying acceleration coefficients. Electronics Letters, 53(20), 1360-1362. * **C_PSO**: Liu, B., Wang, L., Jin, Y. H., Tang, F., & Huang, D. X. (2005). Improved particle swarm optimization combined with chaos. Chaos, Solitons & Fractals, 25(5), 1261-1271. * **CL_PSO**: Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE transactions on evolutionary computation, 10(3), 281-295. * **PFA - Pathfinder Algorithm** * **OriginalPFA**: Yapici, H., & Cetinkaya, N. (2019). A new meta-heuristic optimizer: Pathfinder algorithm. Applied Soft Computing, 78, 545-568. * **PSS - Pareto-like Sequential Sampling** * **OriginalPSS**: Shaqfa, M., & Beyer, K. (2021). Pareto-like sequential sampling heuristic for global optimisation. Soft Computing, 25(14), 9077-9096. ### Q * **QSA - Queuing Search Algorithm** * **OriginalQSA**: Zhang, J., Xiao, M., Gao, L., & Pan, Q. (2018). Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems. Applied Mathematical Modelling, 63, 464-490. * **BaseQSA**: The developed version * **OppoQSA**: Zheng, X. and Nguyen, H., 2022. A novel artificial intelligent model for predicting water treatment efficiency of various biochar systems based on artificial neural network and queuing search algorithm. Chemosphere, 287, p.132251. * **LevyQSA**: Abderazek, H., Hamza, F., Yildiz, A.R., Gao, L. and Sait, S.M., 2021. A comparative analysis of the queuing search algorithm, the sine-cosine algorithm, the ant lion algorithm to determine the optimal weight design problem of a spur gear drive system. Materials Testing, 63(5), pp.442-447. * **ImprovedQSA**: Nguyen, B.M., Hoang, B., Nguyen, T. and Nguyen, G., 2021. nQSV-Net: a novel queuing search variant for global space search and workload modeling. Journal of Ambient Intelligence and Humanized Computing, 12(1), pp.27-46. ### R * **RUN - RUNge Kutta optimizer**: * **OriginalRUN**: Ahmadianfar, I., Heidari, A. A., Gandomi, A. H., Chu, X., & Chen, H. (2021). RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method. Expert Systems with Applications, 181, 115079. ### S * **SA - Simulated Annealling** **OriginalSA**: Kirkpatrick, S., Gelatt Jr, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598), 671-680. **GaussianSA**: Van Laarhoven, P. J., Aarts, E. H., van Laarhoven, P. J., & Aarts, E. H. (1987). Simulated annealing (pp. 7-15). Springer Netherlands. **SwarmSA**: My developed version * **SSpiderO - Social Spider Optimization** * **OriginalSSpiderO**: Cuevas, E., Cienfuegos, M., ZaldíVar, D., & Pérez-Cisneros, M. (2013). A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Systems with Applications, 40(16), 6374-6384. * **SMO - Spider Monkey Optimization** * **DevSMO**: Bansal, J. C., Sharma, H., Jadon, S. S., & Clerc, M. (2014). Spider monkey optimization algorithm for numerical optimization. Memetic computing, 6(1), 31-47. * **SOS - Symbiotic Organisms Search**: * **OriginalSOS**: Cheng, M. Y., & Prayogo, D. (2014). Symbiotic organisms search: a new metaheuristic optimization algorithm. Computers & Structures, 139, 98-112. * **SSpiderA - Social Spider Algorithm** * **OriginalSSpiderA**: James, J. Q., & Li, V. O. (2015). A social spider algorithm for global optimization. Applied Soft Computing, 30, 614-627. * **SCA - Sine Cosine Algorithm** * **OriginalSCA**: Mirjalili, S. (2016). SCA: a sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120-133. * **BaseSCA**: Attia, A.F., El Sehiemy, R.A. and Hasanien, H.M., 2018. Optimal power flow solution in power systems using a novel Sine-Cosine algorithm. International Journal of Electrical Power & Energy Systems, 99, pp.331-343. * **SRSR - Swarm Robotics Search And Rescue** * **OriginalSRSR**: Bakhshipour, M., Ghadi, M. J., & Namdari, F. (2017). Swarm robotics search & rescue: A novel artificial intelligence-inspired optimization approach. Applied Soft Computing, 57, 708-726. * **SBO - Satin Bowerbird Optimizer** * **OriginalSBO**: Moosavi, S. H. S., & Bardsiri, V. K. (2017). Satin bowerbird optimizer: a new optimization algorithm to optimize ANFIS for software development effort estimation. Engineering Applications of Artificial Intelligence, 60, 1-15. * **BaseSBO**: The developed version * **SHO - Spotted Hyena Optimizer** * **OriginalSHO**: Dhiman, G., & Kumar, V. (2017). Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 114, 48-70. * **SSO - Salp Swarm Optimization** * **OriginalSSO**: Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163-191. * **SFO - Sailfish Optimizer** * **OriginalSFO**: Shadravan, S., Naji, H. R., & Bardsiri, V. K. (2019). The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Engineering Applications of Artificial Intelligence, 80, 20-34. * **ImprovedSFO**: Li, L.L., Shen, Q., Tseng, M.L. and Luo, S., 2021. Power system hybrid dynamic economic emission dispatch with wind energy based on improved sailfish algorithm. Journal of Cleaner Production, 316, p.128318. * **SARO - Search And Rescue Optimization** * **OriginalSARO**: Shabani, A., Asgarian, B., Gharebaghi, S. A., Salido, M. A., & Giret, A. (2019). A New Optimization Algorithm Based on Search and Rescue Operations. Mathematical Problems in Engineering, 2019. * **BaseSARO**: The developed version using Levy-flight * **SSDO - Social Ski-Driver Optimization** * **OriginalSSDO**: Tharwat, A., & Gabel, T. (2019). Parameters optimization of support vector machines for imbalanced data using social ski driver algorithm. Neural Computing and Applications, 1-14. * **SLO - Sea Lion Optimization** * **OriginalSLO**: Masadeh, R., Mahafzah, B. A., & Sharieh, A. (2019). Sea Lion Optimization Algorithm. Sea, 10(5). * **ImprovedSLO**: The developed version * **ModifiedSLO**: Masadeh, R., Alsharman, N., Sharieh, A., Mahafzah, B.A. and Abdulrahman, A., 2021. Task scheduling on cloud computing based on sea lion optimization algorithm. International Journal of Web Information Systems. * **Seagull Optimization Algorithm** * **OriginalSOA**: Dhiman, G., & Kumar, V. (2019). Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowledge-based systems, 165, 169-196. * **DevSOA**: The developed version * **Squirrel Search Algorithm** * **OriginalSquirrelSA**: Jain, M., Singh, V., & Rani, A. (2019). A novel nature-inspired algorithm for optimization: Squirrel search algorithm. Swarm and evolutionary computation, 44, 148-175. * **SMA - Slime Mould Algorithm** * **OriginalSMA**: Li, S., Chen, H., Wang, M., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems. * **BaseSMA**: The developed version * **SSA - Sparrow Search Algorithm** * **OriginalSSA**: Jiankai Xue & Bo Shen (2020) A novel swarm intelligence optimization approach: sparrow search algorithm, Systems Science & Control Engineering, 8:1, 22-34, DOI: 10.1080/21642583.2019.1708830 * **BaseSSA**: The developed version * **SPBO - Student Psychology Based Optimization** * **OriginalSPBO**: Das, B., Mukherjee, V., & Das, D. (2020). Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems. Advances in Engineering software, 146, 102804. * **DevSPBO**: The developed version * **SCSO - Sand Cat Swarm Optimization** * **OriginalSCSO**: Seyyedabbasi, A., & Kiani, F. (2022). Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems. Engineering with Computers, 1-25. ### T * **TLO - Teaching Learning Optimization** * **OriginalTLO**: Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303-315. * **BaseTLO**: Rao, R., & Patel, V. (2012). An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. International Journal of Industrial Engineering Computations, 3(4), 535-560. * **ImprovedTLO**: Rao, R. V., & Patel, V. (2013). An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Scientia Iranica, 20(3), 710-720. * **TWO - Tug of War Optimization** * **OriginalTWO**: Kaveh, A., & Zolghadr, A. (2016). A novel meta-heuristic algorithm: tug of war optimization. Iran University of Science & Technology, 6(4), 469-492. * **OppoTWO**: Kaveh, A., Almasi, P. and Khodagholi, A., 2022. Optimum Design of Castellated Beams Using Four Recently Developed Meta-heuristic Algorithms. Iranian Journal of Science and Technology, Transactions of Civil Engineering, pp.1-13. * **LevyTWO**: The developed version using Levy-flight * **ImprovedTWO**: Nguyen, T., Hoang, B., Nguyen, G., & Nguyen, B. M. (2020). A new workload prediction model using extreme learning machine and enhanced tug of war optimization. Procedia Computer Science, 170, 362-369. * **TSA - Tunicate Swarm Algorithm** * **OriginalTSA**: Kaur, S., Awasthi, L. K., Sangal, A. L., & Dhiman, G. (2020). Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Engineering Applications of Artificial Intelligence, 90, 103541. * **TSO - Tuna Swarm Optimization** * **OriginalTSO**: Xie, L., Han, T., Zhou, H., Zhang, Z. R., Han, B., & Tang, A. (2021). Tuna swarm optimization: a novel swarm-based metaheuristic algorithm for global optimization. Computational intelligence and Neuroscience, 2021. ### U ### V * **VCS - Virus Colony Search** * **OriginalVCS**: Li, M. D., Zhao, H., Weng, X. W., & Han, T. (2016). A novel nature-inspired algorithm for optimization: Virus colony search. Advances in Engineering Software, 92, 65-88. * **BaseVCS**: The developed version ### W * **WCA - Water Cycle Algorithm** * **OriginalWCA**: Eskandar, H., Sadollah, A., Bahreininejad, A., & Hamdi, M. (2012). Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, 110, 151-166. * **WOA - Whale Optimization Algorithm** * **OriginalWOA**: Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67. * **HI_WOA**: Tang, C., Sun, W., Wu, W., & Xue, M. (2019, July). A hybrid improved whale optimization algorithm. In 2019 IEEE 15th International Conference on Control and Automation (ICCA) (pp. 362-367). IEEE. * **WHO - Wildebeest Herd Optimization** * **OriginalWHO**: Amali, D., & Dinakaran, M. (2019). Wildebeest herd optimization: A new global optimization algorithm inspired by wildebeest herding behaviour. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-14. * **WDO - Wind Driven Optimization** * **OriginalWDO**: Bayraktar, Z., Komurcu, M., Bossard, J.A. and Werner, D.H., 2013. The wind driven optimization technique and its application in electromagnetics. IEEE transactions on antennas and propagation, 61(5), pp.2745-2757. ### X ### Y ### Z

List of papers used MEALPY

- Min, J., Oh, M., Kim, W., Seo, H., & Paek, J. (2022, October). Evaluation of Metaheuristic Algorithms for TAS Scheduling in Time-Sensitive Networking. In 2022 13th International Conference on Information and Communication Technology Convergence (ICTC) (pp. 809-812). IEEE. - Khozeimeh, F., Sharifrazi, D., Izadi, N. H., Joloudari, J. H., Shoeibi, A., Alizadehsani, R., ... & Islam, S. M. S. (2021). Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients. Scientific Reports, 11(1), 15343. - Rajesh, K., Jain, E., & Kotecha, P. (2022). A Multi-Objective approach to the Electric Vehicle Routing Problem. arXiv preprint arXiv:2208.12440. - Sánchez, A. J. H., & Upegui, F. R. (2022). Una herramienta para el diseño de redes MSMN de banda ancha en líneas de transmisión basada en algoritmos heurísticos de optimización comparados. Revista Ingeniería UC, 29(2), 106-123. - Khanmohammadi, M., Armaghani, D. J., & Sabri Sabri, M. M. (2022). Prediction and Optimization of Pile Bearing Capacity Considering Effects of Time. Mathematics, 10(19), 3563. - Kudela, J. (2023). The Evolutionary Computation Methods No One Should Use. arXiv preprint arXiv:2301.01984. - Vieira, M., Faia, R., Pinto, T., & Vale, Z. (2022, September). Schedule Peer-to-Peer Transactions of an Energy Community Using Particle Swarm. In 2022 18th International Conference on the European Energy Market (EEM) (pp. 1-6). IEEE. - Bui, X. N., Nguyen, H., Le, Q. T., & Le, T. N. Forecasting PM. MINING SCIENCE ANDTECHNOLOGY (Russia), 111. - Bui, X. N., Nguyen, H., Le, Q. T., & Le, T. N. (2022). Forecasting PM 2.5 emissions in open-pit minesusing a functional link neural network optimized by various optimization algorithms. Gornye nauki i tekhnologii= Mining Science and Technology (Russia), 7(2), 111-125. - Doğan, E., & Yörükeren, N. (2022). Enhancement of Transmission System Security with Archimedes Optimization Algorithm. - Ayub, N., Aurangzeb, K., Awais, M., & Ali, U. (2020, November). Electricity theft detection using CNN-GRU and manta ray foraging optimization algorithm. In 2020 IEEE 23Rd international multitopic conference (INMIC) (pp. 1-6). IEEE. - Pintilie, L., Nechita, M. T., Suditu, G. D., Dafinescu, V., & Drăgoi, E. N. (2022). Photo-decolorization of Eriochrome Black T: process optimization with Differential Evolution algorithm. In PASEW-22, MESSH-22 & CABES-22 April 19–21, 2022 Paris (France). Eminent Association of Pioneers. - LaTorre, A., Molina, D., Osaba, E., Poyatos, J., Del Ser, J., & Herrera, F. (2021). A prescription of methodological guidelines for comparing bio-inspired optimization algorithms. Swarm and Evolutionary Computation, 67, 100973. - Gottam, S., Nanda, S. J., & Maddila, R. K. (2021, December). A CNN-LSTM Model Trained with Grey Wolf Optimizer for Prediction of Household Power Consumption. In 2021 IEEE International Symposium on Smart Electronic Systems (iSES)(Formerly iNiS) (pp. 355-360). IEEE. - Darius, P. S., Devadason, J., & Solomon, D. G. (2022, December). Prospects of Ant Colony Optimization (ACO) in Various Domains. In 2022 4th International Conference on Circuits, Control, Communication and Computing (I4C) (pp. 79-84). IEEE. - Ayub, N., Irfan, M., Awais, M., Ali, U., Ali, T., Hamdi, M., ... & Muhammad, F. (2020). Big data analytics for short and medium-term electricity load forecasting using an AI techniques ensembler. Energies, 13(19), 5193. - Biundini, I. Z., Melo, A. G., Coelho, F. O., Honório, L. M., Marcato, A. L., & Pinto, M. F. (2022). Experimentation and Simulation with Autonomous Coverage Path Planning for UAVs. Journal of Intelligent & Robotic Systems, 105(2), 46. - Yousaf, I., Anwar, F., Imtiaz, S., Almadhor, A. S., Ishmanov, F., & Kim, S. W. (2022). An Optimized Hyperparameter of Convolutional Neural Network Algorithm for Bug Severity Prediction in Alzheimer’s-Based IoT System. Computational Intelligence and Neuroscience, 2022. - Xu, L., Yan, W., & Ji, J. (2023). The research of a novel WOG-YOLO algorithm for autonomous driving object detection. Scientific reports, 13(1), 3699. - Costache, R. D., Arabameri, A., Islam, A. R. M. T., Abba, S. I., Pandey, M., Ajin, R. S., & Pham, B. T. (2022). Flood susceptibility computation using state-of-the-art machine learning and optimization algorithms. - Del Ser, J., Osaba, E., Martinez, A. D., Bilbao, M. N., Poyatos, J., Molina, D., & Herrera, F. (2021, December). More is not always better: insights from a massive comparison of meta-heuristic algorithms over real-parameter optimization problems. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-7). IEEE. - Rustam, F., Aslam, N., De La Torre Díez, I., Khan, Y. D., Mazón, J. L. V., Rodríguez, C. L., & Ashraf, I. (2022, November). White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images. In Healthcare (Vol. 10, No. 11, p. 2230). MDPI. - Neupane, D., Kafle, S., Gurung, S., Neupane, S., & Bhattarai, N. (2021). Optimal sizing and financial analysis of a stand-alone SPV-micro-hydropower hybrid system considering generation uncertainty. International Journal of Low-Carbon Technologies, 16(4), 1479-1491. - Liang, R., Le-Hung, T., & Nguyen-Thoi, T. (2022). Energy consumption prediction of air-conditioning systems in eco-buildings using hunger games search optimization-based artificial neural network model. Journal of Building Engineering, 59, 105087. - He, Z., Nguyen, H., Vu, T. H., Zhou, J., Asteris, P. G., & Mammou, A. (2022). Novel integrated approaches for predicting the compressibility of clay using cascade forward neural networks optimized by swarm-and evolution-based algorithms. Acta Geotechnica, 1-16. - Xu, L., Yan, W., & Ji, J. (2022). The research of a novel WOG-YOLO algorithm forautonomous driving object detection. - Nasir Ayub, M. I., Awais, M., Ali, U., Ali, T., Hamdi, M., Alghamdi, A., & Muhammad, F. Big Data Analytics for Short and Medium Term Electricity Load Forecasting using AI Techniques Ensembler. - Xie, C., Nguyen, H., Choi, Y., & Armaghani, D. J. (2022). Optimized functional linked neural network for predicting diaphragm wall deflection induced by braced excavations in clays. Geoscience Frontiers, 13(2), 101313. - Hakemi, S., Houshmand, M., & Hosseini, S. A. (2022). A Dynamic Quantum-Inspired Genetic Algorithm with Lengthening Chromosome Size. - Kashifi, M. T. City-Wide Crash Risk Prediction and Interpretation Using Deep Learning Model with Multi-Source Big Data. Available at SSRN 4329686. - Nguyen, H., & Hoang, N. D. (2022). Computer vision-based classification of concrete spall severity using metaheuristic-optimized Extreme Gradient Boosting Machine and Deep Convolutional Neural Network. Automation in Construction, 140, 104371. - Zheng, J., Lu, Z., Wu, K., Ning, G. H., & Li, D. (2020). Coinage-metal-based cyclic trinuclear complexes with metal–metal interactions: Theories to experiments and structures to functions. Chemical Reviews, 120(17), 9675-9742. - Van Thieu, N., Barma, S. D., Van Lam, T., Kisi, O., & Mahesha, A. (2023). Groundwater level modeling using Augmented Artificial Ecosystem Optimization. Journal of Hydrology, 617, 129034. - Mo, Z., Zhang, Z., Miao, Q., & Tsui, K. L. (2022). Intelligent Informative Frequency Band Searching Assisted by a Dynamic Bandit Tree Method for Machine Fault Diagnosis. IEEE/ASME Transactions on Mechatronics. - Dangi, D., Chandel, S. T., Dixit, D. K., Sharma, S., & Bhagat, A. (2023). An Efficient Model for Sentiment Analysis using Artificial Rabbits Optimized Vector Functional Link Network. Expert Systems with Applications, 119849. - Dey, S., Roychoudhury, R., Malakar, S., & Sarkar, R. (2022). An optimized fuzzy ensemble of convolutional neural networks for detecting tuberculosis from Chest X-ray images. Applied Soft Computing, 114, 108094. - Mousavirad, S. J., & Alexandre, L. A. (2022). Population-based JPEG Image Compression: Problem Re-Formulation. arXiv preprint arXiv:2212.06313. - Tsui, K. L. Intelligent Informative Frequency Band Searching Assisted by A Dynamic Bandit Tree Method for Machine Fault Diagnosis. - Neupane, D. (2020). Optimal Sizing and Performance Analysis of Solar PV-Micro hydropower Hybrid System in the Context of Rural Area of Nepal (Doctoral dissertation, Pulchowk Campus). - LaTorre, A., Molina, D., Osaba, E., Poyatos, J., Del Ser, J., & Herrera, F. Swarm and Evolutionary Computation. - Vieira, M. A. (2022). Otimização dos custos operacionais de uma comunidade energética considerando transações locais em “peer-to-peer” (Doctoral dissertation). - Toğaçar, M. (2022). Using DarkNet models and metaheuristic optimization methods together to detect weeds growing along with seedlings. Ecological Informatics, 68, 101519. - Toğaçar, M. (2021). Detection of segmented uterine cancer images by Hotspot Detection method using deep learning models, Pigeon-Inspired Optimization, types-based dominant activation selection approaches. Computers in Biology and Medicine, 136, 104659. - Khan, N. A Short Term Electricity Load and Price Forecasting Model Based on BAT Algorithm in Logistic Regression and CNN-GRU with WOA. - Yelisetti, S., Saini, V. K., Kumar, R., & Lamba, R. (2022, May). Energy Consumption Cost Benefits through Smart Home Energy Management in Residential Buildings: An Indian Case Study. In 2022 IEEE IAS Global Conference on Emerging Technologies (GlobConET) (pp. 930-935). IEEE. - Nguyen, H., Cao, M. T., Tran, X. L., Tran, T. H., & Hoang, N. D. (2022). A novel whale optimization algorithm optimized XGBoost regression for estimating bearing capacity of concrete piles. Neural Computing and Applications, 1-28. - Hirsching, C., de Jongh, S., Eser, D., Suriyah, M., & Leibfried, T. (2022). Meta-heuristic optimization of control structure and design for MMC-HVdc applications. Electric Power Systems Research, 213, 108371. - Amelin, V., Gatiyatullin, E., Romanov, N., Samarkhanov, R., Vasilyev, R., & Yanovich, Y. (2022). Black-Box for Blockchain Parameters Adjustment. IEEE Access, 10, 101795-101802. - Ngo, T. Q., Nguyen, L. Q., & Tran, V. Q. (2022). Novel hybrid machine learning models including support vector machine with meta-heuristic algorithms in predicting unconfined compressive strength of organic soils stabilised with cement and lime. International Journal of Pavement Engineering, 1-18. - Zhu, Y., & Iiduka, H. (2021). Unified Algorithm Framework for Nonconvex Stochastic Optimization in Deep Neural Networks. IEEE Access, 9, 143807-143823. - Hakemi, S., Houshmand, M., KheirKhah, E., & Hosseini, S. A. (2022). A review of recent advances in quantum-inspired metaheuristics. Evolutionary Intelligence, 1-16. - Das, A., Das, S. R., Panda, J. P., Dey, A., Gajrani, K. K., Somani, N., & Gupta, N. (2022). Machine learning based modelling and optimization in hard turning of AISI D6 steel with newly developed AlTiSiN coated carbide tool. arXiv preprint arXiv:2202.00596. - Yelisetti, S., Saini, V. K., Kumar, R., Lamba, R., & Saxena, A. (2022). Optimal energy management system for residential buildings considering the time of use price with swarm intelligence algorithms. Journal of Building Engineering, 59, 105062. - Valdés, G. T. (2022). Algoritmo para la detección de vehículos y peatones combinando CNN´ sy técnicas de búsqueda. - Sallam, N. M., Saleh, A. I., Ali, H. A., & Abdelsalam, M. M. (2023). An efficient EGWO algorithm as feature selection for B-ALL diagnoses and its subtypes classification using peripheral blood smear images. Alexandria Engineering Journal, 68, 39-66.

--- Developed by: [Thieu](mailto:nguyenthieu2102@gmail.com?Subject=MEALPY_QUESTIONS) @ 2022