PSO and the Traveling Salesman Problem: An Intelligent Optimization Approach

TL;DR

This paper adapts Particle Swarm Optimization for the Traveling Salesman Problem, integrating local search techniques and benchmarking its performance against other algorithms, highlighting its strengths and limitations.

Contribution

It introduces a novel discrete PSO approach for TSP, combining permutation encoding with local search to enhance solution quality.

Findings

PSO performs well on small to medium TSP instances.

Local search techniques improve PSO solutions.

Performance declines on larger problems due to local optima.

Abstract

The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem that aims to find the shortest possible route that visits each city exactly once and returns to the starting point. This paper explores the application of Particle Swarm Optimization (PSO), a population-based optimization algorithm, to solve TSP. Although PSO was originally designed for continuous optimization problems, this work adapts PSO for the discrete nature of TSP by treating the order of cities as a permutation. A local search strategy, including 2-opt and 3-opt techniques, is applied to improve the solution after updating the particle positions. The performance of the proposed PSO algorithm is evaluated using benchmark TSP instances and compared to other popular optimization algorithms, such as Genetic Algorithms (GA) and Simulated Annealing (SA). Results show that PSO performs well for small to medium-sized problems, though its performance diminishes for larger instances due to difficulties in escaping local optima. This paper concludes that PSO is a promising approach for solving TSP, with potential for further improvement through hybridization with other optimization techniques.