A Hybrid Immune Evolutionary Algorithm for Global Optimization Search
Optimization is an important issue in many kinds of application areas, whereas expediting optimizing process and jumping out of the local optimums are keys in optimization researches. This article presents an immune evolutionary algorithm for optimizing search in continuous space. The proposed algorithm adopts immune network modal & evolutionary strategy, adjusts self-adaptively the metrics of evolutionary space on immune affinity, such as the evolutionary steps and directions. The algorithm realizes search diversity by restraining most individuals within one immune shapespace measured in restrain radius. The experimental results on multimodal functions show that the proposed algorithm got the whole optimal solutions and a lot of suboptimal ones in lesser amount of evolutionary generations and minor populations compared with the contrasted algorithms, such as CSA, GA and aiNet, and the effect of global optimizing capability are verified with excellent population diversity.
Immune network evolutionary strategy multimodal optimization
Zhu Li
Network Center, Chengdu Sport University, Chengdu, P.R. China
国际会议
长沙
英文
523-526
2010-05-11（万方平台首次上网日期，不代表论文的发表时间）