摘要
针对人工鱼群算法(artificial fish swarm algorithm,AFSA)多峰寻优能力不足的问题,提出了一种免疫人工鱼群网络算法。应用改进的觅食行为,提升了算法的局部寻优能力;采用免疫网络调节机理,保持了人工鱼群多样性,不断探寻新的局部峰值;执行模式搜索法(pattern search method,PSM),完成精英人工鱼群的精细搜索。仿真实验结果表明,该算法具有较强全局优化能力和局部优化能力,且搜索到每个最优解都达到了理想值。
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 op- timization. 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 solutionby 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.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2013年第2期452-456,共5页
Systems Engineering and Electronics
基金
国家自然科学基金(51175510)
航空科学基金(20111396011)资助课题
关键词
多峰优化
鱼群算法
免疫网络
模式搜索法
multimodal optimization
artificial fish swarm algorithm （AFSA）
immune network
pattern search method （PSM）
