A Two-State Dynamic Decomposition-Based Evolutionary Algorithm for Handling Many-Objective Optimization Problems

Simple item page
dc.citation.issue3
dc.citation.volume11
dc.contributor.authorXing L
dc.contributor.authorLi J
dc.contributor.authorCai Z
dc.contributor.authorHou F
dc.contributor.editorPan L
dc.contributor.editorCui Z
dc.contributor.editorGarg H
dc.date.accessioned2024-12-05T22:53:43Z
dc.date.available2024-12-05T22:53:43Z
dc.date.issued2023-01-17
dc.description.abstractDecomposition-based many-objective evolutionary algorithms (D-MaOEAs) are brilliant at keeping population diversity for predefined reference vectors or points. However, studies indicate that the performance of an D-MaOEA strongly depends on the similarity between the shape of the reference vectors (points) and that of the PF (a set of Pareto-optimal solutions symbolizing balance among objectives of many-objective optimization problems) of the many-objective problem (MaOP). Generally, MaOPs with expected PFs are not realistic. Consequently, the inevitable weak similarity results in many inactive subspaces, creating huge difficulties for maintaining diversity. To address these issues, we propose a two-state method to judge the decomposition status according to the number of inactive reference vectors. Then, two novel reference vector adjustment strategies, set as parts of the environmental selection approach, are tailored for the two states to delete inactive reference vectors and add new active reference vectors, respectively, in order to ensure that the reference vectors are as close as possible to the PF of the optimization problem. Based on the above strategies and an efficient convergence performance indicator, an active reference vector-based two-state dynamic decomposition-base MaOEA, referred to as ART-DMaOEA, is developed in this paper. Extensive experiments were conducted on ART-DMaOEA and five state-of-the-art MaOEAs on MaF1-MaF9 and WFG1-WFG9, and the comparative results show that ART-DMaOEA has the most competitive overall performance.
dc.description.confidentialfalse
dc.edition.edition2023
dc.identifier.citationXing L, Li J, Cai Z, Hou F. (2023). A Two-State Dynamic Decomposition-Based Evolutionary Algorithm for Handling Many-Objective Optimization Problems. Mathematics. 11. 3.
dc.identifier.doi10.3390/math11030493
dc.identifier.eissn2227-7390
dc.identifier.elements-typejournal-article
dc.identifier.number493
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/72218
dc.languageEnglish
dc.publisherMDPI (Basel, Switzerland)
dc.publisher.urihttps://www.mdpi.com/2227-7390/11/3/493
dc.relation.isPartOfMathematics
dc.rights(c) 2023 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectdecomposition-based MaOEA
dc.subjectactive reference vector
dc.subjecttwo-state method
dc.subjectART-DMaOEA
dc.titleA Two-State Dynamic Decomposition-Based Evolutionary Algorithm for Handling Many-Objective Optimization Problems
dc.typeJournal article
pubs.elements-id459972
pubs.organisational-groupOther
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
459972 PDF.pdf
Size:
719.78 KB
Format:
Adobe Portable Document Format
Description:
Published version.pdf
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
9.22 KB
Format:
Plain Text
Description:
Download
Collections
Journal Articles