| 本文已被：浏览 3790次 下载 7694次 |
码上扫一扫！ |
|
|
| 基于分解的演化多目标优化算法综述 |
|
高卫峰1, 刘玲玲1, 王振坤2,3, 公茂果4
|
|
1.西安电子科技大学 数学与统计学院, 陕西 西安 710126;2.南方科技大学 系统设计与智能制造学院, 广东 深圳 518055;3.南方科技大学 计算机科学与工程系, 广东 深圳 518055;4.西安电子科技大学 电子工程学院, 陕西 西安 710071
|
|
| 摘要: |
| 基于分解的演化多目标优化算法(MOEA/D)的基本思想是将一个多目标优化问题转化成一系列子问题 (单目标或者多目标)来进行优化求解. 自2007年提出以来, MOEA/D受到了国内外学者的广泛关注, 已经成为最具代表性的演化多目标优化算法之一. 总结过去13年中关于MOEA/D的一些研究进展, 具体内容包括: (1)关于MOEA/D的算法改进; (2) MOEA/D在超多目标优化问题及约束优化问题上的研究; (3) MOEA/D在一些实际问题上的应用. 然后, 实验对比几个具有代表性的MOEA/D改进算法. 最后, 指出一些MOEA/D未来的研究方向. |
| 关键词: 多目标优化 演化算法 分解 MOEA/D |
| DOI：10.13328/j.cnki.jos.006672 |
| 分类号: |
| 基金项目:国家自然科学基金(61772391,62106186);陕西省自然科学基础研究计划(2022JQ-670,2020JM-178) |
|
| Survey on Multiobjective Optimization Evolutionary Algorithm Based on Decomposition |
|
GAO Wei-Feng1, LIU Ling-Ling1, WANG Zhen-Kun2,3, GONG Mao-Guo4
|
|
1.School of Mathematics and Statistics, Xidian University, Xi’an 710126, China;2.School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518055, China;3.Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China;4.School of Electronic Engineering, Xidian University, Xi’an 710071, China
|
| Abstract: |
| The basic concept of the multiobjective optimization evolutionary algorithm based on decomposition (MOEA/D) is to transform a multiobjective optimization problem into a set of subproblems (single-objective or multiobjective) for optimization solutions. Since MOEA/D was proposed in 2007, it has attracted extensive attention from Chinese and international scholars and become one of the most representative multiobjective optimization evolutionary algorithms. This study summarizes the research progress on MOEA/D in the past thirteen years. The advances include algorithm improvements of MOEA/D, research of MOEA/D on many-objective optimization and constraint optimization,and application of MOEA/D in some practical issues. Then, several representative improved algorithms of MOEA/D are compared through experiments. Finally, the study presents several potential research topics of MOEA/D in the future. |
| Key words: multiobjective optimization evolutionary algorithm decomposition MOEA/D |
