An Improved Immune Clone Selection Algorithm and Applications in Multimodal Function Optimization
In order to solve the existing problems that are the population size required to be determined by the experience,weaker multi-peak search capability and longer training time for Castro clone selection algorithm. We propose a new immune clone selection algorithm based on real coding and adaptive zoom mutation method,which is able to dynamically determine the population size,owns strong global and local search capabilities and can search the global optimal points and possibly the greatest number of local extreme points. The average iteration number of multi-peak searching decreased to almost a quarter compared with Castro clone selection algorithm. Simulation results also show that this algorithm reduces the average running time by 89.8％,based on which multimodal function optimization results have been significantly improved.
Artificial Immune system clone selection real coding adaptive zoom mutation
Han Li Sun Liying Chang Zhiying
School of Automation Engineering Northeast Dianli University,Jilin City,Jilin Province,132012,China
国际会议
南宁
英文
17-20
2010-12-10（万方平台首次上网日期，不代表论文的发表时间）