Low-dimensional space modeling-based differential evolution for large scale global optimization problems

dc.contributor.authorFonseca, Thiago Henrique Lemos
dc.contributor.authorNassar, Silvia Modesto
dc.contributor.authorde Oliveira, Alexandre César Muniz
dc.contributor.authorAgard, Bruno
dc.contributor.funderCoordenação de Aperfeiçoamento de Pessoal de Nível Superioren
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2023-01-26T13:14:29Z
dc.date.available2023-01-26T13:14:29Z
dc.date.issued2022-12-07
dc.date.updated2023-01-26T12:04:57Z
dc.description.abstractLarge-Scale Global Optimization (LSGO) has been an active research field. Part of this interest is supported by its application to cutting-edge research such as Deep Learning, Big Data, and complex real-world problems such as image encryption, real-time traffic management, and more. However, the high dimensionality makes solving LSGO a significant challenge. Some recent research deal with the high dimensionality by mapping the optimization process to a reduced alternative space. Nonetheless, these works suffer from the changes in the search space topology and the loss of information caused by the dimensionality reduction. This paper proposes a hybrid metaheuristic, so-called LSMDE (Low-dimensional Space Modeling-based Differential Evolution), that uses the Singular Value Decomposition to build a low-dimensional search space from the features of candidate solutions generated by a new SHADE-based algorithm (GM-SHADE). GM-SHADE combines a Gaussian Mixture Model (GMM) and two specialized local algorithms: MTS-LS1 and L-BFGS-B, to promote a better exploration of the reduced search space. GMM mitigates the loss of information in mapping high-dimensional individuals to low-dimensional individuals. Furthermore, the proposal does not require prior knowledge of the search space topology, which makes it more flexible and adaptable to different LSGO problems. The results indicate that LSMDE is the most efficient method to deal with partially separable functions compared to other state-of-the-art algorithms and has the best overall performance in two of the three proposed experiments. Experimental results also show that the new approach achieves competitive results for non-separable and overlapping functions on the most recent test suite for LSGO problems.en
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (Finance Code 001); Science Foundation Ireland (grant no.SFI/16/RC/3918 CONFIRM; Marie Sklodowska-Curie grant agreement no. 847.577)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationFonseca, T. H. L., Nassar, S. M., de Oliveira, A. C. M. and Agard, B. (2022) 'Low-dimensional space modeling-based differential evolution for large scale global optimization problems', IEEE Transactions on Evolutionary Computation. doi: 10.1109/TEVC.2022.3227440en
dc.identifier.doi10.1109/TEVC.2022.3227440en
dc.identifier.eissn1941-0026
dc.identifier.issn1089-778X
dc.identifier.journaltitleIEEE Transactions on Evolutionary Computationen
dc.identifier.urihttps://hdl.handle.net/10468/14138
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.projectinfo:eu-repo/grantAgreement/EC/H2020::MSCA-COFUND-FP/847577/EU/Smart Manufacturing Advanced Research Training for Industry 4.0/SMART 4.0
dc.rights© 2022, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectClustering algorithmsen
dc.subjectDifferential evolutionen
dc.subjectDimensionality reductionen
dc.subjectGaussian mixture modelen
dc.subjectHeuristic algorithmsen
dc.subjectOptimizationen
dc.subjectSearch problemsen
dc.subjectSingular value decompositionen
dc.subjectSociologyen
dc.subjectStatisticsen
dc.titleLow-dimensional space modeling-based differential evolution for large scale global optimization problemsen
dc.typeArticle (peer-reviewed)en
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