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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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
Publisher Name: Springer, Berlin, Heidelberg
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