Competitive-cooperative coevolutionary immune-dominant clone selection algorithm for solving the traveling salesman problem
竞争合作型协同进化免疫算法及其在旅行商问题中的应用

To improve the convergence performance of artificial immune algorithm, we propose a competitivecooperative coevolutionary immune-dominant clone selection algorithm(CCCICA). Enlightened by the knowledge of ecological environment and population competition, we incorporate the cooperative evolution in ecology into the artificial immune system. The affinity maturation of antibody is enhanced by the local optimization of the immune-dominance, the clone expansion and the adaptive dynamic hyper-hybrid mutation and other factors in the species. The population diversity is evaluated and adjusted by the locus information entropy. All subpopulations share one memory which is also used as a leader set consisting of the dominant representatives of each evolved subpopulation. The high level memory is optimized by using the immune genetic crossover operator. Several best individuals are migrated to subpopulations from the top excellent population based on the predefined condition. Through those operations, information is shared among populations for co-evolution. The results demonstrate good performance of the CCCICA in solving the traveling salesman problem(TSP) when compared with other modern intelligent algorithms.