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Hybrid immune dynamic optimization algorithm based on dual population
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

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