Abstract
This paper presents an improved chaotic ant swarm (ICAS) by introducing three strategies, which are comprehensive learning strategy, search bound strategy and refinement search strategy, into chaotic ant swarm (CAS) for solving optimization problems. The first two strategies are employed to update ants' positions, which preserve the diversity of the swarm so that the ICAS discourages premature convergence. In addition, the refinement search strategy is adopted to increase the solution quality in the ICAS. Simulations show that the ICAS significantly enhances solution accuracy and convergence stability of the CAS. © 2013 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
Li, Y. Y., Li, L. X., & Peng, H. P. (2013). Improving chaotic ant swarm performance with three strategies. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7928 LNCS, pp. 268–277). https://doi.org/10.1007/978-3-642-38703-6_32
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