摘要
基于生物免疫系统的自适应学习、免疫记忆、抗体多样性及动态平衡维持等功能,提出一种动态多目标免疫优化算法处理动态多目标优化问题.算法设计中,依据自适应ζ邻域及抗体所处位置设计抗体的亲和力,基于Pa-reto控制的概念,利用分层选择确定参与进化的抗体,经由克隆扩张及自适应高斯变异,提高群体的平均亲和力,利用免疫记忆、动态维持和Average linkage聚类方法,设计环境识别规则和记忆池,借助3种不同类型的动态多目标测试问题,通过与出众的动态环境优化算法比较,数值实验表明所提出算法解决复杂动态多目标优化问题具有较大潜力.
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 affinity 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. Furthermore, the average linkage method and several functions of immune memory and dynamic balance maintenance 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 dynamic multi-objective benchmark problems. Simulations show that the algorithm has great potential in solving dynamic multi-objective optimization problems.
出处
《智能系统学报》
2007年第5期68-77,共10页
CAAI Transactions on Intelligent Systems
基金
国家自然科学基金资助项目(60565002)
关键词
动态多目标优化
时变Pareto面
环境跟踪
自适应ξ邻域
免疫算法
dynamic multi-objective optimization
time-varying Pareto front
environment tracking
adaptive -neighborhood
immune algorithm.
