# PCMTSP

**Repository Path**: bzy1999/pcmtsp

## Basic Information

- **Project Name**: PCMTSP
- **Description**: No description available
- **Primary Language**: Python
- **License**: MulanPSL-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-04-26
- **Last Updated**: 2024-05-06

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# A Benchmark Test Suite for PCMTSP

This repository provides code for reading PCMTSP instances, generating PCMTSP instances, and existing test sets of three scales.

#### About PCMTSP

Multiple Traveling Salesmen Problem with Pivot Cities (PCMTSP) refers to an extended version of classical Multiple Traveling Salesmen Problem (MTSP), which addresses practical logistics and transportation scenarios involving pivot cities or nodes where repeated visits are allowed. In PCMTSP, in addition to the basic conditions that require multiple salesmen to return to the starting point after completing visits to all cities, and that each city is visited at least once, there are also specific pivot cities that need to be visited multiple times by different salesmen.

#### Contents

1. PCMTSP.py: Python code for reading PCMTSP instances. 
- The parameter `path`: the storage path of the PCMTSP instance.

2. generator_instances.py: Python code for generating PCMTSP instances. 
- The parameter `tsp`: Object of type TSP, representing an instance of TSP. 
- The parameter `num_pivot`: The number of pivot cities to be generated. 
- The parameter `pivot_type`: The type of pivot cities. There are three types available:
  **'intensive'** : The generated pivot cities have a high number of visits.
  **'sparse'** : The generated pivot cities have fewer visits.
  **'normal'** : The number of visits for generated pivot cities is randomly distributed, between intensive and sparse.

3. data/PCMTSPLIB: Existing PCMTSP test sets of three scales, consisting of small, medium and large scale instances

#### Notice

This code is only for academic use and if you use this code, please kindly cite the following related papers:

[1] Xin-Ai Dou, Qiang Yang*, Xu-Dong Gao, Zhen-Yu Lu, and Jun Zhang, "Benchmark for Multiple Traveling Salesman Problem with Visiting Constraints", in International Conference on Machine Intelligence Theory and Applications, 2023.

[2] Cong Bao, Qiang Yang*, Xu-Dong Gao, Zhen-Yu Lu, and Jun Zhang, "Ant Colony Optimization with Shortest Distance Biased Dispatch for Visiting Constrained Multiple Traveling Salesmen Problem," in Proc. Genet. Evol. Comput. Conf. Companion, pp. 77-80, 2022.

[3] Cong Bao, Qiang Yang*, Xu-Dong Gao, and Jun Zhang, "A Comparative Study on Population-Based Evolutionary Algorithms for Multiple Traveling Salesmen Problem with Visiting Constraints," in IEEE Symp. Ser. Comput. Intell., pp. 01-08, 2021.

[4] Cong Bao, Qiang Yang*, Xu-Dong Gao, and Zhen-Yu Lu, "Genetic Algorithm with Adapted Crossover Operators for Multiple Traveling Salesmen Problem with Visiting Constraints," in IEEE Int. Conf. Syst. Man Cybern., pp. 3033-3039, 2022.

[4] Xin-Ai Dou, Qiang Yang*, Pei-Lan Xu, Xu-Dong Gao, and Zhen-Yu Lu, "Comparative Study on Different Encoding Strategies for Multiple Traveling Salesmen Problem," in IEEE Int. Conf. Syst. Man Cybern., pp. 1437-1442, 2023.

[5] Nuo Xu, Deming Wu, Qiang Yang* et al., "Ant Colony Optimization for Multiple Travelling Salesmen Problem with Pivot Cities," in Int. Conf. Adv. Comput. Intell., pp. 1-8, 2023.

[6] Bing Sun, Chuan Wang*, Qiang Yang* et al., "Ant Colony Optimization for Balanced Multiple Traveling Salesmen Problem," in Int. Conf. Comput. Sci. Comput. Intell., pp. 476-481, 2021.

[7] Xin-Xin Liu, Dong Liu, Qiang Yang* et al., "Comparative Analysis of Five Local Search Operators on Visiting Constrained Multiple Traveling Salesmen Problem," in IEEE Symp. Ser. Comput. Intell., pp. 01-08, 2021.

If you have any question, please contact boziyang666@gmail.com.