Particle Swarm Optimization Algorithm Source Code for TSP Problem

This source code implements a Particle Swarm Optimization (PSO) algorithm specifically designed to solve the 50-city Traveling Salesman Problem (TSP). The implementation features key components including particle position initialization, velocity updates using cognitive and social parameters, and fitness evaluation based on total path distance. The algorithm maintains a population of candidate solutions (particles) that navigate the search space by tracking personal best positions and global best solutions. PSO is a population-based optimization technique inspired by collective animal behavior such as bird flocking, where particles collaboratively search for optimal solutions by balancing exploration and exploitation. For TSP applications, the algorithm efficiently searches for the shortest possible route that visits each city exactly once and returns to the origin city. The code includes mechanisms for handling discrete optimization problems through appropriate position encoding and neighborhood operators. This implementation demonstrates practical applications in route optimization, resource allocation, and combinatorial problem-solving. The modular code structure allows easy adaptation to various NP-hard problems by modifying the fitness function and constraint handling mechanisms. Parameter configuration options enable tuning of swarm size, inertia weight, and acceleration coefficients to optimize performance for different problem scales.