This repository contains an implementation of Particle Swarm Optimization to solve Traveling Salesman Problem. Requirements:
- Python 3.X
- NumPy package
The PSO variant used in code is Velocity-Tentative PSO presented in the following paper: https://www.researchgate.net/publication/279948232_Velocity_Tentative_PSO_An_Optimal_Velocity_Implementation_based_Particle_Swarm_Optimization_to_Solve_Traveling_Salesman_Problem
It is essential to read following paper for understanding first application of PSO for solving TSP Problem: https://www.researchgate.net/publication/4052712_Particle_swarm_optimization_for_traveling_salesman_problem The algorithm is Swap Sequence based PSO (SSPSO). The graph mentioned in this paper is included in 'cities14.py' file in the form of adjacency matrix. The optimal tour with cost of 30.8785 is obtained by the presented code
'TSPusingsspso.py' is main python implementation file which needs to be . All functions and TSP graph are implemented in the user-defined module 'psofortspmodule.py' which is imported in 'TSPusingsspso.py'
TSP problem is initialized for 29 cities (adjacency matrix in 'cities29.py') for which optimum tour is NOT known to me. I encourage all of you to show the best cost TSP tour around the graph.
Code implemented and maintained by Sudip C. Mahajan; email: [email protected]