Particle Swarm Optimization for Traveling Salesman Problem with Included Dataset

This article presents an implementation of Particle Swarm Optimization (PSO) for solving the Traveling Salesman Problem (TSP). TSP represents a classic optimization challenge extensively applied in logistics and transportation sectors, aiming to identify the shortest possible route visiting each city in a given set exactly once. The algorithm implementation must account for inter-city distances and path sequencing constraints.

We demonstrate our PSO approach using a practical dataset instance. Our implementation incorporates key algorithmic components including position updates using velocity vectors, personal best tracking, and global best synchronization. The code calculates Euclidean distances between cities and employs fitness evaluation to optimize route length. Through multiple experimental runs with parameter tuning, the algorithm consistently achieves improved path solutions, demonstrating convergence toward shorter routes with each iteration.

The results confirm PSO as an effective metaheuristic for TSP optimization. By employing swarm intelligence principles with proper neighborhood topologies and inertia weight adjustments, our implementation successfully minimizes total travel distance while maintaining computational efficiency. The solution demonstrates practical applicability for real-world routing scenarios with consistent performance across test cases.