Chaotic Immune PSO Algorithm for Traveling Salesman Problem

  • Xiaofeng Chen
  • Zhenhua Tan
  • Guangming Yang
  • Yan Xiangshuai
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 143)

Abstract

In connection with the drawback that the Particle Swarm Optimization algorithm is easy to fall in local extremum in solving the TSP, based on the learning of the existing results, and take advantage of the ergodicity and the intrinsic randomness of chaos, and inspired by the immune mechanism of organism immune system, and introduce the chaos optimization method and the information processing mechanisms of the immune system to PSO, we proposed a chaotic immune particle swarm optimization algorithm, and make use of the algorithm to solving the TSP. Experimental results show that the algorithm can distinguished improve the convergence performance of PSO algorithm, and the efficiency of searching has been improved significantly.

Keywords

PSO algorithm Chaos Artificial Immune TSP 

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References

  1. 1.
    Yang, W.-B., Zhao, Y.-W.: Improved simulated annealing algorithm for TSP. Computer Engineering and Applications 46 (15), 34–36 (2010)Google Scholar
  2. 2.
    Lin, W., Delgado-Frias, J.G., Gause, D.C., et al.: Hybrid newton-raphson genetic algorithm for the traveling salesman problem. Cybernetics and Systems 26(4), 387–412 (1995)MATHCrossRefGoogle Scholar
  3. 3.
    Tan, K.C., Tang, H., Ge, S.S.: On parameter settings of Hopfield networks applied to traveling salesman problems. IEEE Transactions on Circuits and Systems 52(5), 994–1002 (2005)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Shan, L., Qiang, H., Li, J., Wang, Z.-Q.: Chaotic optimization algorithm based on Tent map. Control and Decision 20(2), 179–182 (2005)MATHGoogle Scholar
  5. 5.
    Yuan, Z., Yang, L., Wu, Y.: Chaitic patticle sarm optimization algorithm for traveling salesman problem. In: Proceedings of the IEEE International Conference on Automation and Logistics, vol. 1, pp. 121–124 (2007)Google Scholar
  6. 6.
    Timmis, J., Neal, M., Hunt, J.: An artificial immune system for data analysis. Biosystem 55(1), 143–150 (2000)CrossRefGoogle Scholar
  7. 7.
    de Castro, L., Von Zuben, F.: Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation 6(3), 239–251 (2002)CrossRefGoogle Scholar
  8. 8.
    Jiao, L.C., Du, H.F.: Development and Prospect of the Artificial Immune System. Acta Electronica Sinica 31(9), 73–80 (2003)Google Scholar
  9. 9.
    Xie, K.G., Zeng, X.H.: Comparative Analysis between Immune Algorithm and Other Random Searching Algorithms. Journal of Chongqing University 26(11), 14–17 (2003)Google Scholar
  10. 10.
    Clerc, M.: Discrete Particle Swarm Optimization Illustrated by the Traveling Salesman Problem, http://www.mauriceclerc.net
  11. 11.
    Chen, C., Xu, C., Bie, R., Gao, X.Z.: Artificial Immune Recognition. System for DNA Microarray Data Analysis. In: Fourth Intenational Conference on Natural Computation ICNC 2008, vol. 6, pp. 633–637 (2008)Google Scholar
  12. 12.
    Chang, Z., Zhu, G.: Application on Express Delivery of an Immune Genetic Algorithm Based on Machine Learning. In: Second International Symposium on Computational Intelligence and Design ISCID 2009, vol. 2, pp. 165–167 (2009)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Xiaofeng Chen
    • 1
  • Zhenhua Tan
    • 1
  • Guangming Yang
    • 1
  • Yan Xiangshuai
    • 1
  1. 1.Software CollegeNortheastern UniversityShenyang CityChina

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