电子学报 ›› 2020, Vol. 48 ›› Issue (8): 1596-1604.DOI: 10.3969/j.issn.0372-2112.2020.08.019

• 学术论文 • 上一篇    下一篇

基于自适应支配准则的高维多目标进化算法

孙文静, 李军华, 黎明   

  1. 南昌航空大学江西省图像处理与模式识别重点实验室, 江西南昌 330063
  • 收稿日期:2019-11-16 修回日期:2020-01-10 出版日期:2020-08-25 发布日期:2020-08-25
  • 通讯作者: 李军华
  • 作者简介:孙文静 女,汉族,1995年生于安徽广德,现为南昌航空大学硕士研究生,主要研究方向为进化算法. E-mail:18970941060@163.com
  • 基金资助:
    国家自然科学基金(No.61440049,No.61866025,No.61866026);江西省自然科学基金(No.2018BAB202025);江西省优势科技创新团队计划(No.2018BCB24008);江西省研究生创新基金(No.YC2019-S400)

Adaptive Dominance Criterion Based Evolutionary Algorithm for Many-objective Optimization

SUN Wen-jing, LI Jun-hua, LI Ming   

  1. Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition, Nanchang Hangkong University, Nanchang, Jiangxi 330063, China
  • Received:2019-11-16 Revised:2020-01-10 Online:2020-08-25 Published:2020-08-25

摘要: 基于松弛支配的高维多目标进化算法(Many-objective Evolutionary Algorithms,MaOEAs)由于能够有效地提高区分解的能力,受到广泛关注,但该类大多数算法处理不同目标的优化问题时普适性较差.针对这个问题,本文提出一种基于自适应支配准则的高维多目标进化算法(Adaptive Dominance Criterion Based Evolutionary Algorithm for Many-objective Optimization,ADCEA).首先,自适应准则(Adaptive Dominance Criterion,ADC)根据目标空间中相邻解间的角度信息和目标数目,设计一种自适应小生境方法,并结合收敛性指标信息,实现对候选解的非支配排序.然后,为了进一步增强种群的多样性,在环境选择中引入参考向量分割种群技术;最后,构建合理的适应度函数,并根据适应度值大小选取收敛性和多样性较好的非支配解集.实验证明,本文所提的方法在处理不同目标的优化问题时普适性提高,并在平衡种群的收敛性和多样性上取得显著效果.

关键词: 松弛支配, 高维多目标进化算法, 普适性, 自适应支配准则

Abstract: Relaxed dominance relation based evolutionary algorithms for many-objective optimization(MaOEAs)are widely concerned because they effectively improve the ability to identify solutions,however,the most of these algorithms show poor versatility in solving different objectives of optimization problems.To address this issue,this paper proposes an adaptive dominance criterion based evolutionary algorithm for many-objective optimization(ADCEA).Firstly,the ADC designs an adaptive niche method based on the angle information and the number of objectives between adjacent solutions in the objective space,and combines the convergence indicator information to achieve non-dominated sorting of candidate solutions.Then,in order to further enhance the diversity of the population,the reference vector population technique is introduced in the environment selection.Finally,a reasonable fitness function is constructed,and the non-dominated solution set with better convergence and diversity is selected according to the fitness value.The experimental results demonstrate that the proposed method improves the versatility in solving different objectives of optimization problems,and achieves significant effects in balancing convergence and diversity.

Key words: relaxed dominance relations, many-objective evolutionary algorithms, versatility, adaptive dominance criterion

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