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AuthorSong, Aijuan
AuthorWu, Guohua
AuthorSuganthan, P. N.
AuthorPedrycz, Witold
Available date2023-02-15T08:21:00Z
Publication Date2022-08-16
Publication NameIEEE Transactions on Evolutionary Computation
Identifierhttp://dx.doi.org/10.1109/TEVC.2022.3199413
CitationSong, A., Wu, G., Suganthan, P. N., & Pedrycz, W. (2022). Automatic variable reduction. IEEE Transactions on Evolutionary Computation, 27(4), 1027-1041.
ISSN1089-778X
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85136854156&origin=inward
URIhttp://hdl.handle.net/10576/40066
AbstractA variable reduction strategy (VRS) is an effective method to accelerate the optimization process of evolutionary algorithms (EAs) by simplifying the corresponding optimization problems. Unfortunately, the VRS is manually realized in a trial-and-error manner currently. To boost the efficiency of VRS and enable a more extensive application, we propose a variable reduction optimization problem (VROP) to represent a decision space with the smallest sets of variables. Thereafter, a heuristic rule-based automatic variable reduction algorithm (AVRA) is designed to address the VROP. AVRA sequentially reduces variables by considering several sophisticated designed heuristic rules, which search for a variable that can be utilized to represent as many variables as possible and that can be represented by the smallest set of variables possible. With AVRA, the decision variables of an optimization problem can be automatically grouped into reduced variables and core variables, where core variables can represent reduced variables and the entire decision space. During the optimization process, we only need to search the core variables to optimize the problem. Therefore, the dimensionality of the decision space can be reduced by AVRA, subsequently, simplifying the complexity of the problem and improving the search efficiency of EAs. To testify the effectiveness of AVRA, we blend AVRA with several promising EAs to solve two types of challenging problems: continuous equality constrained optimization problems and nonlinear equations systems. Extensive experiments verify that an EA with AVRA outperforms the standard EAThe source code of the paper is available at http://faculty.csu.edu.cn/guohuawu/zh_CN/zdylm/193832/list/index.htm.
SponsorNational Natural Science Foundation of China under Grant 62073341
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Subjectautomatic variable reduction algorithm
equality constrained optimization problem
Evolutionary algorithm
heuristic rule
Mathematical models
Nonlinear equations
nonlinear equations system
Optimization
Principal component analysis
Reactive power
Search problems
Standards
variable reduction optimization problem
variable reduction strategy
TitleAutomatic variable reduction
TypeArticle
Pagination1027-1041
Issue Number4
Volume Number27
ESSN1941-0026
dc.accessType Abstract Only


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