Immune clonal coevolutionary algorithm for dynamic multiobjective optimization

被引：0
作者：
Ronghua Shang
Licheng Jiao
Yujing Ren
Jia Wang
Yangyang Li
机构：
[1] Xidian University,Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China
来源：
Natural Computing | 2014年 / 13卷
关键词：
Dynamic multiobjective optimization; Immune clonal selection; Coevolution;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, a new evolutionary algorithm, called immune clonal coevolutionary algorithm (ICCoA) for dynamic multiobjective optimization (DMO) is proposed. On the basis of the basic principles of artificial immune system, the proposed algorithm adopts the immune clonal selection to solve DMO problems. In addition, the theory of coevolution is incorporated in ICCoA in global operation to preserve the diversity of Pareto-fronts. Moreover, coevolutionary competitive and cooperative operation is designed to enhance the uniformity and the diversity of the solutions. In comparison with NSGA-II, immune clonal algorithm for DMO and direction-based method, the simulation results obtained on 5 difficult test problems and on related performance metrics suggest that ICCoA can achieve better distributed solutions and be very effective in maintaining the uniformity of Pareto-fronts.
引用
收藏
页码：421 / 445
页数：24
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