混沌-克隆进化算法

Chaos-Clone Based Evolutionary Algorithm

将混沌优化机制和免疫克隆进化算法有机结合,用混沌浮点数编码代替克隆选择算法的二进制编码,利用混沌随机序列产生初始种群,保证初始种群的多样性。对高亲和度抗体采用混沌扰动策略,对抗体根据其亲和度大小加以不同的混沌扰动;混沌扰动系数随进化代数而变化,进化前期加速搜索,进化后期加速收敛。对低亲和度抗体采用混沌再生策略,保持种群多样性。对5个复杂函数的优化试验结果表明,该算法优于混沌优化算法和克隆选择算法。

A Chaos--clone based evolutionary algorithm （CCEA） was proposed by integrating chaos search and clonal selection algorithm （CLONALG）. In CCEA the chaotic floating point numbers code was used to replace the binary code of CLONALG, and the initial antibody population was produced by the chaos random serial. The algorithm adopted a chaotic disturbance strategy for the antibodies with high affinity, and added the different chaotic disturbance to an antibody according to its affinity to antigen; the disturbance factor changes with the evolutionary generation so as to speed search during prophase and convergence during anaphase. CCEA uses a chaos to reshuffle operation for those antibodies with low affinity to maintain the diversity of the population. Simulation results for 5 comprehensive benchmark functions demonstrate that the CCEA has better performance than both the chaos optimization and CLONALG individually used.