Abstract: A novel Multi-Objective Immune Algorithm based on Memory Clonal Selection（MOIA-MCS） is proposed in this paper by introducing the mechanism of clonal selection in organismal immune system.A kind of affinity calculational methods is reasonably constructed in the new algorithm which searches not only for the Pareto solution roundly in the antibody group，but also for parcel in memory group.So it can improve the searching efficiency and convergence.Finally，the performance comparison has been made between the new method and the traditional multi-objective evolutionary algorithm NSGA-II by using six typical testing functions in algorithm simulation experiment.Experimental results indicate that the proposed approach can resolve the problem of multi-objective effectively and has better performances.

Key words: immune principle, memory clonal selection, multi-objective, Pareto optimal solution, affinity

摘要： 借鉴生物免疫原理中克隆选择机理，设计了一种基于记忆克隆选择的多目标免疫算法。该算法构建了一种亲和度的快速计算方法，并在抗体种群全局搜索Pareto解的同时，也在记忆单元进行局部搜索，有效地提高了搜索效率和收敛性。选取了六种典型的多目标优化函数进行算法仿真测试研究，并与经典的多目标进化算法NSGA-II进行了比较。仿真研究结果证明了新算法在保证种群分布度的同时，拥有比NSGA-II更好的收敛性和速度。

关键词: 免疫原理, 记忆克隆选择, 多目标, Pareto解, 亲和度