Many-Objective Evolutionary Algorithm Based on Dynamic Decomposition and Angle Penalty Distance

WANG Xu-jian; ZHANG Feng-gan; YAO Min-li

doi:10.12263/DZXB.20230541

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Many-Objective Evolutionary Algorithm Based on Dynamic Decomposition and Angle Penalty Distance

PAPERS | 更新时间：2025-12-08

- Many-Objective Evolutionary Algorithm Based on Dynamic Decomposition and Angle Penalty Distance
- ACTA ELECTRONICA SINICA Vol. 52, Issue 8, Pages: 2773-2785(2024)
- 作者机构：
  
  火箭军工程大学，陕西西安 710025
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62001500;62071480)
- DOI：10.12263/DZXB.20230541
  CLC： TP18;
- Received：13 June 2023，
  
  Revised：2023-09-19，
  
  Published：25 August 2024
- 稿件说明：
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王旭健, 张峰干, 姚敏立. 基于动态分解和角度惩罚距离的高维多目标进化算法[J]. 电子学报, 2024, 52(08): 2773-2785.

WANG Xu-jian, ZHANG Feng-gan, YAO Min-li. Many-Objective Evolutionary Algorithm Based on Dynamic Decomposition and Angle Penalty Distance[J]. Acta Electronica Sinica, 2024, 52(08): 2773-2785.
王旭健, 张峰干, 姚敏立. 基于动态分解和角度惩罚距离的高维多目标进化算法[J]. 电子学报, 2024, 52(08): 2773-2785. DOI：10.12263/DZXB.20230541

WANG Xu-jian, ZHANG Feng-gan, YAO Min-li. Many-Objective Evolutionary Algorithm Based on Dynamic Decomposition and Angle Penalty Distance[J]. Acta Electronica Sinica, 2024, 52(08): 2773-2785. DOI：10.12263/DZXB.20230541

摘要

多个领域的优化可归纳为高维多目标优化问题，高维多目标进化算法是解决此类问题的有效方法，然而该方法普遍存在收敛性和多样性较难平衡的问题.针对此问题，本文提出一种基于动态分解和角度惩罚距离的高维多目标进化算法.该算法基于动态分解将种群分成多个类，此过程无需预先设定参考向量，可根据种群自身分布信息进行分解.之后，基于改进的角度惩罚距离从每类中选择个体，从而平衡收敛性与多样性.此外，设计基于Pareto支配、拐点、

近邻角度三原则的锦标赛匹配选择机制.本文算法与9种高维多目标进化算法在27例高维多目标优化测试题上进行对比实验.实验结果表明，本文算法能有效解决不同类型的高维多目标优化问题，并且在不同目标个数上具有较好的稳定性.

Abstract

The optimization problems in multiple areas can be modelled as many-objective optimization problems

which can be solved using many-objective evolutionary algorithms. However

it is difficult to balance convergence and diversity. To tackle this issue

this paper proposes a many-objective evolutionary algorithm based on dynamic decomposition and modified angle penalty distance referred to as DAEA (Duplication Analysis based Evolutionary Algorithm). DAEA decomposes the whole population into multiple clusters through dynamic decomposition

which is exempt from the predefined reference vectors and makes full use of the distribution information of the population itself to decompose. Then

DAEA selects solutions from each cluster based on modified angle penalty distance to balance convergence and diversity. Besides

DAEA operates mating selection according to Pareto dominance

knee points

and

-nearest angle binary tournament selection. Compared with nine many-objective evolutionary algorithms on 27 many-objective optimization problems

DAEA is effective on many-objective optimization problems with various shapes of Pareto front and stable on different numbers of objectives.

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Keywords

references

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