精英免疫克隆选择的协同进化粒子群算法

提出一种精英免疫克隆选择的协同进化粒子群算法（Elite immune clonal selection co-evolutionary particle swarm optimization，EICS-CPSO）.算法借鉴了协同进化思想和精英策略，基于精英种群与普通群体并行协同进化框架.高适应度的精英个体组成精英团体，运用自适应小波变异的免疫克隆选择算子对精英团体进行提升引导操作.普通种群间个体极值采用柯西交互学习机制提高微粒个体极值收敛性能；迁移操作进一步推进了整体信息共享与协同进化.实验结果表明该算法收敛精度快且全局搜索能力强，且具有较好的动态优化性能.实验分析表明该算法对参数不敏感，易于使用.

Co-Evolutionary Particle Swarm Optimization Algorithm Based on Elite Immune Clonal Selection

A novel Elite immune clonal selection co-evolutionary particle swarm optimization algorithm (named,EICS-CPSO) is proposed based on the elite strategy and co-evolutionary mechanism.The algorithm is consisting of one elite subpopulation and several normal subpopulations based on collaborative computing frame.The elite individuals having high fitness from each normal subpopulation will be selected into the elite subpopulation,during the evolution process.The elite subpopulation will be promoted by the immune clonal selection operator with adaptive wavelet mutation.Furthermore,a simple Cauchy learning operator is utilized for accelerating the convergence speed of the pbest particles while the migration scheme is employed for the information exchange between elite subpopulation and normal subpopulations.The performance of the proposed algorithm is verified through a suite of standard benchmark functions,which shows a faster convergence and global search ability and also has a good dynamic optimization performance.Moreover,the parameters of the EICS-CPSO are analyzed in experiments and the results show that EICS-CPSO is insensitive to parameters and easy to use.