Abstract: A new multi-objective immune algorithm based on multi-population is proposed by the use of genetic algorithms parallel mechanism to avoid local convergence.Each population interacts mutually information by the shift of excellent individual.This can effectively promote the diversity of population，and accelerate convergence rate.The performance comparison has been made between the new method and the traditional multi-objective evolutionary algorithm NSGA-II by using several typical testing functions in algorithms simulation experiment.Experimental results suggest that the proposed approach can resolve the problem of multi-objective effectively and has better performances.

摘要： 借鉴遗传算法中采用并行机制避免局部收敛的思想，提出了一种基于多种群的多目标免疫算法。在该算法中建立多个子种群分别进行免疫操作，子种群之间通过优秀个体转移进行信息交换，可有效地提高种群的多样性，加速收敛。采用几种典型的多目标优化函数进行实验，并同常用的多目标遗传算法NSGA-II进行比较，仿真结果表明了该算法能有效解决多目标优化问题且具有一定的优越性。