Abstract:

An immune artificial fish swarm network algorithm is proposed to deal with the problem of inefficient searching that the artificial fish swarm algorithm (AFSA) has difficulty in solving multimodal function optimization. In the algorithm, the local searching capacity can be enhanced by using improved preying behavior. Referred to immune network theory, the diversity of artificial fish swarm is maintained and new local solutions can be found continuously. The pattern search method (PSM) is introduced to obtain the local optimum solution by its good local extremum search ability. Simulation results show that the proposed algorithm has excellent global optimization performance and good local extremum search ability and can give satisfactory solutions.