Novel Hierarchical Immune Algorithm for TSP Solution
In order to solve traveling salesman problem more efficiently using artificial immune algorithm,a two floor model based on multiple sub populations immune evolution as well as hierarchical local optimization immunodominance clonal selection algorithm(HLOICSA) was put forward.To quickly obtain the global optimum,multiple sub populations were operated by bottom floor immune operators:local optimization immunodominance,clonal selection,antibody diversity amelioration based on locus information entropy,multiple sub populations were also operated by top floor genetic operators:selection,crossover,mutation.Through those operators,diversity of antibody sub population distribution and excellent antibody affinity maturation was enhanced,the balance between in the depth and breadth of the search optimizing was acquired.Experimental results indicate that the algorithm has a remarkable quality of the global convergence reliability and convergence velocity.