Paper Info
Title
A Survey of Weight Vector Adjustment Methods for Decomposition-Based Multiobjective Evolutionary Algorithms
Abstract
Multiobjective evolutionary algorithms based on decomposition (MOEA/D) have attracted tremendous attention and achieved great success in the fields of optimization and decision-making. MOEA/Ds work by decomposing the target multiobjective optimization problem (MOP) into multiple single-objective subproblems based on a set of weight vectors. The subproblems are solved cooperatively in an evolutionary algorithm framework. Since weight vectors define the search directions and, to a certain extent, the distribution of the final solution set, the configuration of weight vectors is pivotal to the success of MOEA/Ds. The most straightforward method is to use predefined and uniformly distributed weight vectors. However, it usually leads to the deteriorated performance of MOEA/Ds on solving MOPs with irregular Pareto fronts. To deal with this issue, many weight vector adjustment methods have been proposed by periodically adjusting the weight vectors in a random, predefined, or adaptive way. This article focuses on weight vector adjustment on a simplex and presents a comprehensive survey of these weight vector adjustment methods covering the weight vector adaptation strategies, theoretical analyses, benchmark test problems, and applications. The current limitations, new challenges, and future directions of weight vector adjustment are also discussed.
Year
DOI
Venue
2020
10.1109/TEVC.2020.2978158
IEEE Transactions on Evolutionary Computation
Keywords
DocType
Volume
Decomposition-based MOEA,multiobjective evolutionary algorithms based on decomposition (MOEA/D),multiobjective evolutionary algorithms,weight vector adjustment
Journal
24
Issue
ISSN
Citations 
4
1089-778X
7
PageRank 
References 
Authors
0.39
58
5
Name
Order
Citations
PageRank
Xiaoliang Ma118218.51
Yanan Yu271.07
Xiaodong Li3156084.64
Yutao Qi470.39
Zexuan Zhu598957.41