关键词: 人工鱼群算法, 多峰优化, 模式搜索法, 优胜劣汰, 聚群行为

Abstract: In order to deal with the problems of inefficient searching and low accuracy of Artificial Fish Swarm Algorithm （AFSA） for multimodal function optimization, an improved AFSA for multimodal function optimization was proposed. In the algorithm, the strategy of the survival of the fitter suppression was adopted, eliminating artificial fish which was situated in food with low concentration of similar artificial fish to select elite artificial fish swarm. Optimization for swarming behavior and following behavior contributed to artificial fish careful search in a new optimization trajectory to enhance its local search capacity. Modifying for preying behavior, artificial fish was avoided sinking flat position. In combination with Pattern Search Method (PSM), its local accuracy search capacity was enhanced. The simulation results indicate that the proposed algorithm has stronger global optimization and local optimization capabilities, and the search for each optimal solution accuracy has reached the ideal value, and it is able to be used for complex multimodal function optimization.

Key words: Artificial Fish Swarm Algorithm (AFSA), multimodal function optimization, Pattern Search Method (PSM), survival of the fittest, swarming behavior