Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (11): 3097-3103.DOI: 10.11772/j.issn.1001-9081.2020121895

• Artificial intelligence •     Next Articles

Improved high-dimensional many-objective evolutionary algorithm based on decomposition

Gangzhu QIAO1,2, Rui WANG1, Chaoli SUN1()   

  1. 1.College of Computer Science and Technology,Taiyuan University of Science and Technology,Taiyuan Shanxi 030024,China
    2.School of Data Science and Technology,North University of China,Taiyuan Shanxi 030051,China
  • Received:2020-12-04 Revised:2021-08-07 Accepted:2021-08-18 Online:2021-11-20 Published:2021-11-10
  • Contact: Chaoli SUN
  • About author:QIAO Gangzhu,born in 1975,Ph. D.,professor.His researchinterests include computational intelligence, deep learning
    WANG Rui,born in 1994,M. S. candidate.His research interestsinclude intelligent computing
    SUN Chaoli,born in 1978,Ph. D., professor. Her researchinterests include computational intelligence ,machine learnin.
  • Supported by:
    the National Natural Science Foundation of China(61876123);the Natural Science Foundation of Shanxi Province(201901D111264)

基于分解的高维多目标改进进化算法

乔钢柱1,2, 王瑞1, 孙超利1()   

  1. 1.太原科技大学 计算机科学与技术学院,太原 030024
    2.中北大学 大数据学院,太原 030051
  • 通讯作者: 孙超利
  • 作者简介:乔钢柱(1975一),男,陕西汉阴人,教授,博士,CCF会员,主要研究方向:计算智能、深度学习
    王瑞(1994一),男,山西忻州人,硕士研究生,主要研究方向:智能计算
    孙超利(1978一),女,浙江诸暨人,教授,博士,CCF会员,主要研究方向:计算智能、机器学习。
  • 基金资助:
    国家自然科学基金资助项目(61876123);山西省自然科学基金资助项目(201901D111264)

Abstract:

In the reference vector based high-dimensional many-objective evolutionary algorithms, the random selection of parent individuals will slow down the speed of convergence, and the lack of individuals assigned to some reference vectors will weaken the diversity of population. In order to solve these problems, an Improved high-dimensional Many-Objective Evolutionary Algorithm based on Decomposition (IMaOEA/D) was proposed. Firstly, when a reference vector was assigned at least two individuals in the framework of decomposition strategy, the parent individuals were selected for reproduction of offspring according to the distance from the individual assigned to the reference vector to the ideal point, so as to increase the search speed. Then, for the reference vector that was not assigned at least two individuals, the point with the smallest distance from the ideal point along the reference vector was selected from all the individuals, so that at least two individuals and the reference vector were associated. Meanwhile, by guaranteeing one individual was related to each reference vector after environmental selection, the diversity of population was ensured. The proposed method was tested and compared with other four high-dimensional many-objective optimization algorithms based on decomposition on the MaF test problem sets with 10 and 15 objectives. Experimental results show that, the proposed algorithm has good optimization ability for high-dimensional many-objective optimization problems: the optimization results of the proposed algorithm on 14 test problems of the 30 test problems are better than those of the other four comparison algorithms. Especially, the proposed algorithm has certain advantage on the degradation problem optimization.

Key words: high-dimensional many-objective optimization, reference vector, convergence, diversity, environmental selection

摘要:

针对基于参考向量的高维多目标进化算法中随机选择父代个体会降低算法的收敛速度,以及部分参考向量分配个体的缺失会减弱种群多样性的问题,提出了一种基于分解的高维多目标改进优化算法(IMaOEA/D)。首先,在分解策略框架下,当一个参考向量至少分配了2个个体时,对该参考向量分配的个体根据其到理想点的距离选择父代个体来繁殖子代,从而提高搜索速度。然后,针对未能分配到至少2个个体的参考向量,则从所有个体中选择沿该参考向量和理想点距离最小的点,使得该参考向量至少有2个个体与其相关。同时,确保环境选择后每个参考向量有一个个体与其相关,从而保证种群的多样性。在10个和15个目标的MaF测试问题集上将所提算法与其他4个基于分解的高维多目标优化算法进行了测试对比,实验结果表明所提算法对于高维多目标优化问题具有较好的寻优能力,且该算法在30个测试问题中的14个测试问题上得到的优化结果均优于其他4个对比算法,特别是对于退化问题具有一定的寻优优势。

关键词: 高维多目标优化, 参考向量, 收敛性, 多样性, 环境选择

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