Improved particle swarm optimization for multi-object Traveling Salesman Problems

Abstract

An improved particle swarm optimization algorithm is proposed in this paper. The algorithm draws on the thinking of the greedy algorithm to initialize the particle swarm. Two swarms are used to optimize synchronously, and crossover and mutation operators in genetic algorithm are introduced into the new algorithm. The algorithm is used to solve multi-object Traveling Salesman Problem. We also use this algorithm to solve multi-object TSP of ten scattered attractions in Shan Xi Province. The results show that the algorithm has high convergence speed and convergence ratio. More Pareto optimal are found with this algorithm.