分层协同进化免疫算法及其在TSP问题中的应用

摘要

In order to solve Traveling Salesman Problem(TSP) more efficient using artificial immune algorithm, using for reference of hierarchical and co-evolutionary idea,a two-floor model based on multiple-population immune evolution as well as Hierarchical Co-evolution Immune Algorithm (HCIA) based on competition-cooperation is put forward. Multiple subpopulations are operated by bottom floor immure operators: local optimization immunodominance、clonal expansion and other clonal selection operatas,amelioraion of antibody diversity treed on improved Panicle Swarm Optimization(PSO) algorithm. Multiple subpopulations are also operated by top floor genetic operators: selection,antibody migration、mulation. Through those operators, excellent antibody affinity maturation and diversity of antibody subpopulation distribution was enhanced, the balance between in the depth and breadth of the search-optimizing was acquired. Experimental results for TSP indicate that HCIA has a remarkable quality of the global convergence reliability and convergence velocity.%为提高人工免疫算法求解TSP问题的效率,借鉴分层和协同进化的思想,构造了一种基于多子种群免疫进化的两层框架模型,在此模型的基础上提出了一种基于竞争一合作的分层协同进化免疫算法(Hierarchical Co-evolution Immune Algorithm,HCIA).HCIA通过对若干个子种群进行低层免疫操作:局部最优免疫优势、克隆扩增及克隆选择算子、基于改进粒子群优化算法的抗体多样性改善和高层遗传操作:选择、抗体迁移、变异,增强优秀抗体实现亲和度成熟的机会,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.针对TSP实验结果表明,HCIA具有可靠的全局收敛性及较快的收敛速度.