中文版 | English
Title

Preference-Based Nonlinear Normalization for Multiobjective Optimization

Author
Corresponding AuthorIshibuchi,Hisao
DOI
Publication Years
2023
ISSN
0302-9743
EISSN
1611-3349
Source Title
Volume
13970 LNCS
Pages
563-577
Abstract
Normalization is commonly used in multiobjective evolutionary algorithms (MOEAs) in order to handle multiobjective optimization problems with differently-scaled objectives. The goal of normalization is to obtain uniformly-distributed solutions over the entire Pareto front. However, in practice, such a uniform solution set may not be a well-distributed solution set for decision making when the desired distribution of solutions is not uniform. To obtain a well-distributed solution set that meets the desired distribution, in this paper, we propose a preference-based nonlinear normalization method that transforms the objective space based on the probability integral transform theorem. As a result, the use of a standard MOEA to search for uniformly-distributed solutions in the transformed objective space leads to a desired well-distributed solution set. The proposed method is incorporated in three different MOEAs (i.e., a Pareto dominance-based MOEA, a decomposition-based MOEA, and an indicator-based MOEA). Experimental results demonstrate the flexibility and effectiveness of the proposed method. Our code is available at https://github.com/linjunhe/moea-pn.
Keywords
SUSTech Authorship
First ; Corresponding
Language
English
URL[Source Record]
Scopus EID
2-s2.0-85151052620
Data Source
Scopus
Citation statistics
Cited Times [WOS]:0
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/524288
DepartmentDepartment of Computer Science and Engineering
Affiliation
1.Department of Computer Science and Engineering,Southern University of Science and Technology (SUSTech),Shenzhen,China
2.Department of Electrical and Computer Engineering,National University of Singapore,Singapore,Singapore
First Author AffilicationDepartment of Computer Science and Engineering
Corresponding Author AffilicationDepartment of Computer Science and Engineering
First Author's First AffilicationDepartment of Computer Science and Engineering
Recommended Citation
GB/T 7714
He,Linjun,Nan,Yang,Ishibuchi,Hisao,et al. Preference-Based Nonlinear Normalization for Multiobjective Optimization[C],2023:563-577.
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