An Improved Particle Swarm Optimization for Traveling Salesman Problem

Authors:
Xinmei Liu;Jinrong Su;Yan Han
Affiliations:
School of information and communication engineering, North University of China, Tai Yuan 030051, China;Department of information engineering, business college of Shan Xi University, Tai Yuan 030031, China;School of information and communication engineering, North University of China, Tai Yuan 030051, China
Venue:
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Year:
2009

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Abstract

In allusion to particle swarm optimization being prone to get into local minimum, an improved particle swarm optimization algorithm is proposed. The algorithm draws on the thinking of the greedy algorithm to initialize the particle swarm. Two swarms are used to optimize synchronously. Crossover and mutation operators in genetic algorithm are introduced into the new algorithm to realize the sharing of information among swarms. We test the algorithm with Traveling Salesman Problem with 14 nodes and 30 nodes. The result shows that the algorithm can break away from local minimum earlier and it has high convergence speed and convergence ratio.