Research Progress of Dynamic Multi-objective Optimization Evolutionary Algorithm

被引：0
作者：
Ma Y.-J. [1 ]
Chen M. [1 ]
Gong Y. [1 ]
Cheng S.-S. [1 ]
Wang Z.-Y. [1 ]
机构：
[1] College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2020年 / 46卷 / 11期
基金：
中国国家自然科学基金;
关键词：
Dynamic optimization; Environmental response strategy; Evolutionary algorithm; Multi-objective optimization;
D O I：
10.16383/j.aas.c190489
中图分类号：
学科分类号：
摘要：
Dynamic multi-objective optimization problems (DMOPs) have become a research focus on the engineering optimization, the objective function, constraint functions and related parameters are likely to be changing over time, How to make rapid response to new environment changes by using the historical optimal solution is the key and difficulty of designing dynamic multi-objective optimization evolutionary algorithm (DMOEA). Based on the introduction to DMOEA, this paper analyzes the main research progress of DMOEA based on individual and population level environmental response strategy and multi-strategy mixing in recent years, introduces the performance test function, evaluation index and application of DMOEA in the field of engineering optimization, analyzes the main problems still faced in DMOEA research and giving an outlook to the future research. Copyright © 2020 Acta Automatica Sinica. All rights reserved.
引用
收藏
页码：2302 / 2318
页数：16
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