一类用于函数优化的基于混沌搜索的免疫算法

将混沌优化算法与克隆选择算法相结合,提出了一类基于混沌搜索的免疫算法．首先利用解空间变换将优化变量表示为混沌变量,并将混沌变量编码为抗体．然后,利用混沌变量的遍历性和随机性特点,通过在高亲和力抗体的邻域内进行混沌搜索以实现局部寻优,通过在整个解空间内的混沌搜索来避免陷入局部最优解．数值仿真结果表明该算法具不易陷入局部最优、解的精度高和操作简单等优点．

Chaotic-search-based immune algorithm for function optimization

A chaotic-search-based immune algorithm(CSIA) is proposed by integrating chaos optimization algorithm and clonal selection algorithm.Firstly,optimization variables are expressed by chaotic variables through solution space transformation,and the chaotic variables are coded into an antibody.Secondly,by making use of the characteristics of ergodicity and randomness of chaotic variables,the local search is performed by chaotic search in the neighborhoods of high affinity antibodies,and the chaotic search is carried out in the whole solution space to avoid sticking at local optima. The results of numerical simulation show that the algorithm has advantages,such as avoiding local optima,high precision solution and simple operations.