A Framework to Handle Multimodal Multiobjective Optimization in Decomposition-Based Evolutionary Algorithms

Multimodal multiobjective optimization is to locate (almost) equivalent Pareto optimal solutions as many as possible. While decomposition-based evolutionary algorithms have good performance for multiobjective optimization, they are likely to perform poorly for multimodal multiobjective optimization...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 24; no. 4; pp. 720 - 734
Main Authors: Tanabe, Ryoji, Ishibuchi, Hisao
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Benchmark testing
Decision making
Decomposition
Decomposition-based evolutionary algorithms
Equivalence
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Indexes
multimodal multiobjective optimization
Multiple objective analysis
Optimization
Pareto optimization
Pareto optimum
Performance enhancement
reference vector-based evolutionary algorithms
Solution space
solution space diversity
Spatial diversity
ISSN:1089-778X, 1941-0026
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Multimodal multiobjective optimization is to locate (almost) equivalent Pareto optimal solutions as many as possible. While decomposition-based evolutionary algorithms have good performance for multiobjective optimization, they are likely to perform poorly for multimodal multiobjective optimization due to the lack of mechanisms to maintain the solution space diversity. To address this issue, this article proposes a framework to improve the performance of decomposition-based evolutionary algorithms for multimodal multiobjective optimization. Our framework is based on three operations: 1) assignment; 2) deletion; and 3) addition operations. One or more individuals can be assigned to the same subproblem to handle multiple equivalent solutions. In each iteration, a child is assigned to a subproblem based on its objective vector, i.e., its location in the objective space. The child is compared with its neighbors in the solution space assigned to the same subproblem. The performance of improved versions of six decomposition-based evolutionary algorithms by our framework is evaluated on various test problems regarding the number of objectives, decision variables, and equivalent Pareto optimal solution sets. Results show that the improved versions perform clearly better than their original algorithms.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2019.2949841