基于差异进化的克隆选择算法  

Clonal selection algorithm based on differential evolution

作　　者：彭伟雄[1] 蔡自兴[1] 王勇[1] 刘星宝[1] 

机构地区：[1]中南大学信息科学与工程学院,长沙410083

出　　处：《计算机工程与应用》2011年第14期28-30,72,共4页Computer Engineering and Applications

基　　金：国家自然科学基金 No.60805027;No.90820302;教育部博士点基金(No.200805330005)~~

摘　　要：针对免疫算法在全局优化过程中多样性不足的问题,将差异进化引入克隆变异操作中,提出了一个新的改进的克隆选择算法——基于差异进化的克隆选择算法(DECSA),算法将差异进化和克隆超变异相结合,促进了抗体与抗体之间的信息融合,使得子代抗体继承父代抗体的信息的同时,携带着不同父代个体信息,丰富了抗体种群的多样性,实现了在同一父代抗体周围的多个方向同时进行全局和局部搜索。对13个标准测试函数的测试结果及与已有的算法的比较表明,该算法表现出较好的局部搜索和全局搜索能力。When dealing with global optimization problems,immune algorithm faces the problem of insufficient diversity.This paper incorporates differential evolution into the operation of clone mutation,and proposes a new improved clonal selection algorithm,called DECSA（Clonal Selection Algorithm based on Differential Evolution）,which combines differential evolution with clonal super-mutation.This method promotes the exchange of information between antibody and antibody,lets offspring inherit their parent antibody’s information and carries other parent antibody’s information at the same time and,as a result, enriches the diversity of antibody populations.This method can perform global search and local search in many directions rather than one direction around the identical antibody simultaneously.13 standard functions are used to test the performance of the proposed algorithm and compare the results with the existing algorithms.The results show that the proposed algorithm has a better local search and global search capability

关 键 词：进化计算 免疫算法 差异进化 克隆选择算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]