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영어

The artificial fish swarm algorithm (AFSA) is a heuristic global optimization technique based on population which is easy to understand, good robustness, and not insensitive to initial values. The behavior of fishes has a great impact on the performance of the algorithm, such as global search and convergence speed. At present, there has no general research theory to select behaviors of fishes. In order to deal with this problem, we proposed an improved artificial fish swarm algorithm based on hybrid behavior selection. There are two mainly works in this paper. Firstly, we propose an improved algorithm based on swallowed behavior, which can greatly speed up the convergence. Secondly, in order to deal with the problems of easy to fall into local optimum value, we added breeding behavior to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and faster convergence speed.