An Adaptive Mutation Multi-particle Swarm Optimization for Traveling Salesman Problem

摘要

Traveling Salesman Problem (TSP) is a well-known NP-hard combinatorial optimization problem. The Particle Swarm Optimization has been proven to succeed in lots of problems, but the PSO algorithm is challenging due to a variety of factors such as easy to fall into local optimal solution and the convergence speed is slow in the later. In this paper, we propose an adaptive mutation multi-particle swarm optimization algorithm (AMPSO) to the TSP. The experimental results show that the proposed algorithm can achieves better performance compared to the standard PSO method to solve the TSP.