自适应免疫算法及其对动态函数优化的跟踪

Adaptive Immune Algorithm and Its Track to Dynamic Function Optimization

基于生物免疫系统的自适应学习、记忆、监视等功能,设计适用于高维动态函数优化的自适应免疫算法.算法设计中,利用抗体的学习功能设计抗体动态进化模块;利用基因漂移促成抗体群中非优越抗体重构;利用记忆特性和记忆池动态维持功能,设计由记忆子集合构成的动态记忆池,并经由 Average linkage 保存优秀的记忆细胞;利用动态监视功能建立环境判别规则和初始抗体群的生成规则.该算法结构简单、灵活,以及在不同环境下寻优时间可以动态调节.数值实验比较显示出其优越性和在执行效率、执行效果中寻求权衡的有效性,并且对复杂的高维动态环境优化问题具有较大应用潜力.

An adaptive immune algorithm, based on the functions of the biological immune system such as adaptive learning, memory and surveillance, is proposed to solve high dimensional dynamical function optimization. In the algorithm, with structural simplicity, feasibility, and dynamical regulation of the execution time for different environments, the dynamical evolution and antibody rearrangement are involved. The dynamical memory pool consists of memory subsets related to the characteristics of immune memory and dynamical maintenance of the pool, in which each subset keeps some excellent memory cells obtained by the average linkage. In the meanwhile, dynamical surveillance and memory establish the environmental identification and generation rule of initial antibody populations. Experimental results and comparison illustrate the superiority and the effective trade-off between performance effect and efficiency as well as the potential in the complex dynamical high-dimensional optimization problems.