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.
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.
作者
高卫峰
刘玲玲
王振坤
公茂果
GAO Wei-Feng;LIU Ling-Ling;WANG Zhen-Kun;GONG Mao-Guo(School of Mathematics and Statistics,Xidian University,Xi’an 710126,China;School of System Design and Intelligent Manufacturing,Southern University of Science and Technology,Shenzhen 518055,China;Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen 518055,China;School of Electronic Engineering,Xidian University,Xi’an 710071,China)
出处
《软件学报》
EI
CSCD
北大核心
2023年第10期4743-4771,共29页
Journal of Software
基金
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)。
关键词
多目标优化
演化算法
分解
MOEA/D
multiobjective optimization
evolutionary algorithm
decomposition
MOEA/D
