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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Artificial Fish Swarm Optimization Algorithm Based on Mixed Crossover Strategy
verfasst von : Li-yan Zhuang, Jing-qing Jiang
Erschienen in: Advances in Neural Networks – ISNN 2013
Verlag: Springer Berlin Heidelberg
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The nonlinear constrained optimization problems have been widely used in many fields, such as engineering optimization and artificial intelligence. According to the deficiency of artificial fish swarm algorithm (AFSA), that the artificial fishes walk around aimlessly and randomly or gather in non-global optimal points, a hybrid algorithm-artificial fish swarm optimization algorithm based on mixed crossover strategy is presented. By improving the artificial fish’s behaviors, the genetic operation of mixed crossover strategy is used as a local search strategy of AFSA. So the efficiency of local convergence of AFSA is improved, and the algorithm’s running efficiency and solution quality are improved obviously. Based on test verification for typical functions, it is shown that the hybrid algorithm has some better performance such as fast convergence and high precision.