PSO Implementation for Traveling Salesman Problem in MATLAB Environment

This text describes a MATLAB-based implementation of Particle Swarm Optimization (PSO) for solving the Traveling Salesman Problem (TSP). TSP represents a classic combinatorial optimization challenge that seeks the shortest possible route enabling a salesman to visit all cities exactly once and return to the starting point. Our PSO implementation employs swarm intelligence where particles navigate the solution space through velocity updates and position adjustments based on personal and global best solutions. The MATLAB implementation features key components including: particle initialization with random tours, fitness evaluation using total distance calculation, velocity update equations incorporating cognitive and social parameters, and position updates through permutation operations. The algorithm maintains a balance between exploration and exploitation using inertia weights and acceleration coefficients. During each PSO iteration, particles exchange information and collaboratively search the solution space through movement patterns that combine their individual experiences with swarm intelligence. The MATLAB environment provides efficient matrix operations for handling city coordinates and distance calculations, while utilizing built-in functions for random number generation and vector manipulations. This implementation includes mechanisms for handling constraint violations through repair operators and incorporates local search techniques to enhance solution quality. This PSO-based MATLAB program facilitates better understanding and resolution of TSP problems, offering multiple solution approaches and demonstrating the flexibility of swarm intelligence algorithms in solving complex optimization challenges. The code structure includes modular functions for initialization, evaluation, update operations, and visualization of convergence progress.