一种基于改进的免疫克隆选择的优化方法

Novel Immune Clonal Selection Optimization Programming

通过在克隆选择过程中引入聚类竞争机制,提出了一种免疫聚类竞争的克隆选择算法。采用了抗体聚类、竞争扩增、克隆删除、体细胞高频变异、抗体循环补充等思想及相关算子操作,增强聚类族中的优秀个体获得克隆扩增实现亲和力成熟的机会,提高抗体群分布的多样性,在深度搜索和广度寻优之间取得了平衡。实验仿真及应用结果表明:该算法具有可靠的全局收敛性及较快的收敛速度,将其应用于冶金过程目标优化中取得较好的效果。

By introducing a mechanism of Cluster and Competition in the clonal selection process, a novel immune Cluster and Competition Clonal Selection Algorithm （3CSA ） was proposed. To quickly obtain the global optimum and local optimum, this algorithm adopted the ideas of antibody cluster, compete expansion, clonal elimination, antibody hyper mutation and supplement. Through those operators, the variety of antibody and affinity maturation was enhanced. Simulation illustrated that the algorithm has a remarkable quality of the global convergence reliability and convergence velocity. The method was applied to metallurgical process objective optimization and the satisfactory results were obtained.