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 immune operators:local optimization immunodominance、clonal expansion and other clonal selection operators、amelioration of antibody diversity based on improved Particle Swarm Optimization(PSO) algorithm.Multiple subpopulations are also operated by top floor genetic operators:selection、antibody migration、mutation.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.