Author: ZHANG Weizheng ZHANG Menghua ZHANG Weiwei JIN Baohua WU Huaiguang WANG Hua LI Guoqing | Time: 2020-01-10 | Counts: |
doi:10.16186/j.cnki.1673-9787.2020.1.15
Received:2019/04/13
Revised:2019/05/28
Published:2020/01/15
Hybrid immune dynamic optimization algorithm based on dual population
ZHANG Weizheng, ZHANG Menghua, ZHANG Weiwei, JIN Baohua, WU Huaiguang, WANG Hua, LI Guoqing
School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000 , Henan, China
Abstract:Aiming at the problems of premature convergence and lack of diversity encountered by traditional swarm intelligence algorithms in solving dynamic optimization problems, an immune dynamic optimization algorithm (BPAIS) based on dual population was proposed. Inspired by the innate immune response and adaptive immune response in the biological immune system, the initial population was separated into two populations based on fitness values : innate population and adaptive population. Then, innate immune response was performed on innate populations for global search and diversity maintenance, while adaptive immune response was applied on adaptive population for local search enhancement by using differential evolution method. In addition ,the memory tracking mechanism was introduced to track the local optimal solution when the environment changed. Finally, based on the immune response of dual populations and memory tracking mechanism, a dual population immune dynamic optimization algorithm was proposed. The simulation experiments were performed on the simple test-case generator and moving peaks benchmarks ( MPB ) . The results showed that BPAIS had superior dynamic optimization ability and could effectively track and locate the global optimal solution. Compared with the other algorithms, it was very competitive.
Key words:dual population;innate immune response;adaptive immune response;hybrid immune dynamic optimization;
基于双种群的混合免疫动态优化算法_张卫正.pdf