Abstract
By introducing the information entropy H(X) and mutual information I(X;Y) of information theory into swarm intelligence, the Interaction Optimization Model (IOM) is proposed. In this model, the information interaction process of individuals is analyzed with H(X) and I(X;Y) aiming at solving optimization problems. We call this optimization approach as interaction optimization. In order to validate this model, we proposed a new algorithm for Traveling Salesman Problem (TSP), namely Route-Exchange Algorithm (REA), which is inspired by the information interaction of individuals in swarm intelligence. Some benchmarks are tested in the experiments. The results indicate that the algorithm can quickly converge to the optimal solution with quite low cost.
This work is supported by the National Natural Science Foundation, China (No. 70431003) and the National Basic Research Program, China (2002CB312204).
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References
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence-from Natural to Artificial System. Oxford University Press, New York (1999)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)
Grefenstette, J., Gopal, R., Rosimaita, B., Van Gucht, D.: Genetic algorithms for the traveling salesman problem. In: Proceedings of the International Conference on Genetics Algorithms and their Applications, pp. 160–168 (1985)
Yao, X.: Evolutionary Computation: Theory and Applications. World Scientific, Singapore (1999)
Tan, K.C., Lim, M.H., Yao, X., Wang, L.P. (eds.): Recent Advances in Simulated Evolution and Learning. World Scientific, Singapore (2004)
Liu, J., Zhong, W.C., Liu, F., Jiao, L.C.: Organizational coevolutionary classification algorithm for radar target recognition. Journal of Infrared and Millimeter Waves 23(3), 208–212 (2004)
Han, J., Cai, Q.S.: Emergent Intelligence in AER Model. Chinese Journal of Pattern Recognition and Artificial Intelligence 15(2), 134–142 (2002)
Shannon, C.E.: A mathematical theory of communication. Bell System Technology Journal 27, 397–423 (1948)
Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco (2001)
http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95
Niu, B., Zhu, Y.-l., He, X.-X.: Multi-population Cooperative Particle Swarm Optimization. In: Capcarrère, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds.) ECAL 2005. LNCS (LNAI), vol. 3630, pp. 874–883. Springer, Heidelberg (2005)
Niu, B., Zhu, Y.-l., He, X.-X.: A Multi-population Cooperative Particle Swarm Optimizer for Neural Network Training. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3971, pp. 570–576. Springer, Heidelberg (2006)
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He, X., Zhu, Y., Hu, K., Niu, B. (2006). Information Entropy and Interaction Optimization Model Based on Swarm Intelligence. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881223_18
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DOI: https://doi.org/10.1007/11881223_18
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