Improving Chaotic Ant Swarm Performance with Three Strategies
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.
KeywordsBenchmark Function Chaotic Neural Network Good Exemplar Rastrigin Function Chaotic Search
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