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

被引：0
|
作者：
Wang, Xu-Jian [1 ]
Zhang, Feng-Gan [1 ]
Yao, Min-Li [1 ]
机构：
[1] Rocket Force University of Engineering, Shaanxi, Xi’an
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2024年 / 52卷 / 08期
基金：
中国国家自然科学基金;
关键词：
angle penalty distance; dynamic decomposition; many-objective optimization; multiobjective optimization;
D O I：
10.12263/DZXB.20230541
中图分类号：
学科分类号：
摘要：
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 m-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. © 2024 Chinese Institute of Electronics. All rights reserved.
引用
收藏
页码：2773 / 2785
页数：12
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