Abstract: In order to overcome the shortcomings of the basic ant colony algorithm, such as slow convergence speed, precocity and stagnation, this paper proposes a hybrid ant colony algorithm based on the superior peptide and immune memory (SPIM-ACA) by using the mechanism of immune memory and the superior peptide selection and implantation. SPIM-ACA adds the interior and exterior memory library to the ant colony mode, takes the solutions in the memory library as antibodies and the problem as antigen, and undergoes the construction of solution and the updating of pheromone concentration by using the above mechanism. The results of experiment for solving TSP (traveling salesman problem) indicate that the proposed algorithm is superior to some standard algorithms, such as the basic ant colony algorithm， the immune algorithm and so on, in the respects of the quality and the diversity of the performance.