Abstract: 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. I}o quickly obtain the global optimum,multiple sulrpopulations 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. I}hrough those operators, diversity of antibody sulrpopulation 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.

Key words: Artificial immune algorithm, Traveling salesman problem, Hierarchical, Local optimization immunodomi nance, Clonal selection