ISPO: A New Way to Solve Traveling Salesman Problem

Affiliation(s)

College of Science, China University of Mining & Technology, XuZhou, China；Foundation Departments, Xuzhou Air Force Academy, XuZhou, China.

Foundation Departments, Xuzhou Air Force Academy, XuZhou, China；College of Science, China University of Mining & Technology, XuZhou, China.

College of Science, China University of Mining & Technology, XuZhou, China；Foundation Departments, Xuzhou Air Force Academy, XuZhou, China.

Foundation Departments, Xuzhou Air Force Academy, XuZhou, China；College of Science, China University of Mining & Technology, XuZhou, China.

ABSTRACT

This paper first introduces the concepts of mobile operators and mobile sequence, with which it redefines the rate of particle swarm optimization algorithm and the formula of position updating. Combining this discrete PSO algorithm with neighbors, the paper puts forward Hybrd Particle Swarm Optimization Algorithm, whose effectiveness is verified at the end of this paper.

This paper first introduces the concepts of mobile operators and mobile sequence, with which it redefines the rate of particle swarm optimization algorithm and the formula of position updating. Combining this discrete PSO algorithm with neighbors, the paper puts forward Hybrd Particle Swarm Optimization Algorithm, whose effectiveness is verified at the end of this paper.

Cite this paper

Wang, X. , Mu, A. and Zhu, S. (2013) ISPO: A New Way to Solve Traveling Salesman Problem.*Intelligent Control and Automation*, **4**, 122-125. doi: 10.4236/ica.2013.42017.

Wang, X. , Mu, A. and Zhu, S. (2013) ISPO: A New Way to Solve Traveling Salesman Problem.

References

[1] J. Kennedy and R. C. Eberhart, “Particle Swarm Optimization,” IEEE International Conference on Neural Network, Perth, 1995, pp. 1942-1948.

[2] J. Kennedy and R. C. Eberhart, “A Discrete Binary Version of the Particle Swarm Algorithm,” Proceedings of the World Multiconference on Systemics, Cybernertics and Informatics 1997, IEEE Service Center, Piscataway, 1997, pp. 4104-4109.

[3] J. Kennedy, “Dynamic-Probabilistic Particle Swarms,” Proceedings of the Genetic and Evolutionary Computation Conference, Washington DC, 2005, pp. 201-207.

[4] M. Clerc and J. Kennedy, “The Particle Swarm-Explosion, Stability and Convergence in a Multidimensional Complex Space,” IEEE Transactions on Evolutionary Computation, Vol. 6, No. 1, 2002, pp. 58-73. doi:10.1109/4235.985692

[5] L. Huang, K.-P. Wang and C.-G. Zhou, “Particle Swarm Optimization for Traveling Salesman Problems,” Journal of Jilin University, Vol. 41, No. 4, 2003, pp. 477-480.

[6] S. Gao, B. Han, X. J. Wu, et al., “Solving Traveling Salesman Problem by Hybrid Particle Swarm Optimization Algorithm,” Control and Decision, Vol. 19, No. 11, 2004, pp. 1286-1289.

[7] J. C. Zeng and Z. H. Cui, “A New Unified Model of Particle Swarm Optimization and Its Theoretical Analysis,” Journal of Computer Research and Development, Vol. 43, No. 1, 2006, pp. 96-100. doi:10.1360/crad20060115

[8] L.-P. Fang, P. Chen and S.-H. Liu, “Particle Swarm Optimization with Simulated Annealing for TSP,” Proceeding of the 6th WSEAS International Conference on Artificial intelligence, Knowledge Engineering and Data Bases, Corfu Island, 2008.

[9] Y. Marinakis and M. Marinaki, “A Hybrid Multi-Swarm Particle Swarm Optimization algorithm for the Probabilistic Traveling Salesman Problem,” Computers & Operations Research, Vol. 37, No. 3, 2010, pp. 432-442. doi:10.1016/j.cor.2009.03.004

[10] C. Wang, M. M. Zeng and J. Li, “Solving Traveling Salesman Problems with Time Windows by Genetic Particle Swarm Optimization,” 2008 IEEE Congress on Evolutionary Computation, 2008, pp. 1752-1755.

[1] J. Kennedy and R. C. Eberhart, “Particle Swarm Optimization,” IEEE International Conference on Neural Network, Perth, 1995, pp. 1942-1948.

[2] J. Kennedy and R. C. Eberhart, “A Discrete Binary Version of the Particle Swarm Algorithm,” Proceedings of the World Multiconference on Systemics, Cybernertics and Informatics 1997, IEEE Service Center, Piscataway, 1997, pp. 4104-4109.

[3] J. Kennedy, “Dynamic-Probabilistic Particle Swarms,” Proceedings of the Genetic and Evolutionary Computation Conference, Washington DC, 2005, pp. 201-207.

[4] M. Clerc and J. Kennedy, “The Particle Swarm-Explosion, Stability and Convergence in a Multidimensional Complex Space,” IEEE Transactions on Evolutionary Computation, Vol. 6, No. 1, 2002, pp. 58-73. doi:10.1109/4235.985692

[5] L. Huang, K.-P. Wang and C.-G. Zhou, “Particle Swarm Optimization for Traveling Salesman Problems,” Journal of Jilin University, Vol. 41, No. 4, 2003, pp. 477-480.

[6] S. Gao, B. Han, X. J. Wu, et al., “Solving Traveling Salesman Problem by Hybrid Particle Swarm Optimization Algorithm,” Control and Decision, Vol. 19, No. 11, 2004, pp. 1286-1289.

[7] J. C. Zeng and Z. H. Cui, “A New Unified Model of Particle Swarm Optimization and Its Theoretical Analysis,” Journal of Computer Research and Development, Vol. 43, No. 1, 2006, pp. 96-100. doi:10.1360/crad20060115

[8] L.-P. Fang, P. Chen and S.-H. Liu, “Particle Swarm Optimization with Simulated Annealing for TSP,” Proceeding of the 6th WSEAS International Conference on Artificial intelligence, Knowledge Engineering and Data Bases, Corfu Island, 2008.

[9] Y. Marinakis and M. Marinaki, “A Hybrid Multi-Swarm Particle Swarm Optimization algorithm for the Probabilistic Traveling Salesman Problem,” Computers & Operations Research, Vol. 37, No. 3, 2010, pp. 432-442. doi:10.1016/j.cor.2009.03.004

[10] C. Wang, M. M. Zeng and J. Li, “Solving Traveling Salesman Problems with Time Windows by Genetic Particle Swarm Optimization,” 2008 IEEE Congress on Evolutionary Computation, 2008, pp. 1752-1755.