:A dynamic multi-objective immune optimization algorithm suitable for dynamic multi-objective optimization problems is proposed based on the functions of adaptive learning, immune memory, antibody diversity and dynamic balance maintenance, etc. In the design of the algorithm, the scheme of antibody af- finity was designed based on the locations of adaptive-neighborhood and antibody antibodies participating in evolution were selected by Pareto dominance. In order to enhance the average affinity of the population, clonal proliferation and adaptive Gaussian mutation were adopted to evolve excellent antibodies. Further- more, the average linkage method and several functions of immune memory and dynamic balance mainte- nance were used to design environmental recognition rules and the memory pool. The proposed algorithm was compared against several popular multi-objective algorithms by means of three different kinds of dy- namic multi-objective benchmark problems. Simulations show

动态多目标免疫优化算法及性能测试研究.caj

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