电子学报 ›› 2021, Vol. 49 ›› Issue (11): 2208-2216.DOI: 10.12263/DZXB.20201044

所属专题: 多目标优化

• 学术论文 • 上一篇    下一篇

一种基于角度信息的约束高维多目标进化算法

刘冰洁1, 毕晓君2   

  1. 1.哈尔滨工程大学信息与通信工程学院, 黑龙江 哈尔滨 150001
    2.中央民族大学信息工程学院, 北京 100081
  • 收稿日期:2020-09-22 修回日期:2021-04-07 出版日期:2021-11-25 发布日期:2021-11-25
  • 作者简介:刘冰洁 女, 1989年3月生, 河北秦皇岛人. 哈尔滨工程大学博士研究生. 主要从事进化计算、智能信息处理技术方面的研究. E-mail:993910899@qq.com
    毕晓君(通讯作者) 女, 1964年11月生, 黑龙江哈尔滨人.中央民族大学信息工程学院教授、博士生导师.主要从事智能信息处理技术、数字图像处理、智能优化算法及机器学习的理论与应用方面的研究.E-mail:bixiaojun@hrbeu.edu.cn
  • 基金资助:
    国家自然科学面上基金(51779050)

A Constrained Many‑Objective Evolutionary Algorithm Based on Angle Information

Bing-jie LIU1, Xiao-jun BI2   

  1. 1.College of Information and Communication Engineering,Harbin Engineering University,Harbin,Heilongjiang 150001,China
    2.Department of Information Engineering,Minzu University of China,Beijing 100081,China
  • Received:2020-09-22 Revised:2021-04-07 Online:2021-11-25 Published:2021-11-25

摘要:

目前约束高维多目标进化算法大多注重提高收敛精度, 而收敛速度相对较慢. 为提高算法的收敛速度, 提出一种基于角度信息的约束高维多目标进化算法. 该算法提出基于角度违反度函数的选择操作, 依据动态的收敛性和分布性直接选择较优个体, 提高收敛速度; 此外, 提出了基于差分进化算法的交叉操作, 在不同的进化阶段选用不可行解参与交叉操作, 补偿收敛精度.在标准测试函数集C-DTLZ上进行仿真实验, 并与当前国内外性能优异的4种约束高维多目标进化算法进行对比, 证明了本文算法收敛精度保持良好, 而收敛速度得到了提升, 且目标维数越高提升效果越明显.

关键词: 约束高维多目标优化, 角度违反度, 选择操作, 差分算法, 交叉操作

Abstract:

Most of the current constrained many-objective evolutionary algorithms focus on the convergence accuracy, but the convergence speed is relatively slow. In order to improve the convergence speed, a constrained many-objective evolutionary algorithm based on angle information (CMaOEA-AI) is proposed. In the algorithm, a selection operation based on the angle violation function is proposed to improve the convergence speed, which directly selects the superior individuals according to the dynamic convergence and diversity. Thereafter a crossover operation based on the differential evolutionary algorithm is proposed, which can select the infeasible solutions to participate in the crossover operation at different evolutionary stages. Simulation experiments are performed on the standard test function sets C-DTLZ. Compared with four state-of-the-art constrained many-objective evolutionary algorithms, the proposed algorithm shows good convergence accuracy while the convergence speed is greatly improved, and the higher the objective dimension, the better the effect.

Key words: constrained many-objective optimization, angle violation function, selection operation, differential evolution algorithm, crossover operation

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