Evolutionary algorithm to traveling salesman problems

This paper proposed an improved version of the Particle Swarm Optimization (PSO) approach to solve Traveling Salesman Problems (TSP). This evolutionary algorithm includes two phases. The first phase includes Fuzzy C-Means clustering, a rule-based route permutation, a random swap strategy and a cluster merge procedure. This approach firstly generates an initial non-crossing route, such that the TSP can be solved more efficiently by the proposed PSO algorithm. The use of sub-cluster also reduces the complexity and achieves better performance for problems with a large number of cities. The proposed Genetic-based PSO procedure is then applied to solve the TSP with better efficiency in the second phase. The proposed Genetic-based PSO procedure is applied to TSPs with better efficiency. Fixed runtime performance was used to demonstrate the efficiency of the proposed algorithm for the cases with a large number of cities.

[1]  Kiyotaka Izumi,et al.  A particle-swarm-optimized fuzzy-neural network for voice-controlled robot systems , 2005, IEEE Transactions on Industrial Electronics.

[2]  Junfeng Chen,et al.  Particle swarm optimization with adaptive mutation and its application research in tuning of PID parameters , 2006, 2006 1st International Symposium on Systems and Control in Aerospace and Astronautics.

[3]  Rafael Bello,et al.  Two-Step Particle Swarm Optimization to Solve the Feature Selection Problem , 2007, Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007).

[4]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[5]  Ebroul Izquierdo,et al.  Image Classification using Chaotic Particle Swarm Optimization , 2006, 2006 International Conference on Image Processing.

[6]  James C. Bezdek,et al.  Fuzzy mathematics in pattern classification , 1973 .

[7]  Wei Pang,et al.  Modified particle swarm optimization based on space transformation for solving traveling salesman problem , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[8]  H. Moghaddam,et al.  Feature Subset Selection for Face Detection Using Genetic Algorithms and Particle Swarm Optimization , 2006, 2006 IEEE International Conference on Networking, Sensing and Control.

[9]  Chunguang Zhou,et al.  Fuzzy discrete particle swarm optimization for solving traveling salesman problem , 2004, The Fourth International Conference onComputer and Information Technology, 2004. CIT '04..

[10]  S.G. Ponnambalam,et al.  A Hybrid Discrete Particle Swarm Optimization Algorithm to Solve Flow Shop Scheduling Problems , 2006, 2006 IEEE Conference on Cybernetics and Intelligent Systems.

[11]  Chunguang Zhou,et al.  Particle swarm optimization for traveling salesman problem , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).