关键词: 克隆选择, 免疫算法, TSP, 自适应算法, 群体灾变算法, 记忆库, 接种疫苗

Abstract:

Aiming at the slow convergence speed of the traditional immune clonalselection algorithm (ICA), an adaptive immune clonal selection algorithm without memory(AICA)and adaptive immune clonal selection algorithm with memory(AICAM)are proposed respectively based on the combination of the adaptive algorithm of clona probability, immune probability, and group disaster algorithm. The two proposed algorithms have been applied to the TSP problem. The application of the group disaster algorithm can enhance the diversity of the population and to some extent avoid premature problems. The adaptive algorithm has strong global search ability andweak local search ability at early evolution. Global search ability is weakenedand local search ability is enhanced with the process of evolution in order to find the global optimal point. Simulation results indicate that compared with thetraditional immune clonal selection algorithm(ICA),the proposed algorithms can enhance the diversity of the population, avoid premature problems, and can to some extent accelerate convergence speed.

Key words: clonal selection; immune algorithm; TSP; adaptive algorithm groups; groups disater algorithm; memory; vaccination