Boosting Multiverse Optimizer by Simulated Annealing for Dimensionality Reduction

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dc.contributor.authorMutlag, Wamidh K.
dc.contributor.authorMazher, Wamidh Jalil
dc.contributor.authorIbrahim, Hadeel Tariq
dc.contributor.authorUçan, Osman Nuri
dc.date.accessioned2025-08-15T12:03:21Z
dc.date.available2025-08-15T12:03:21Z
dc.date.issued2025
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
dc.description.abstractBackground: Because of The Multi-Verse Optimizer (MVO) has gained popularity in feature selection due to its strong global and local search capabilities. However, its effectiveness diminishes when tackling high-dimensional datasets due to the exponential growth of the search space and a tendency for premature convergence. Objective: This study aims to enhance MVO’s performance by integrating it with the Simulated Annealing Algorithm (SAA), creating a hybrid model that improves search convergence and optimizes feature selection efficiency. Methods: A High-level Relay Hybrid (HRH) architecture is proposed, where MVO identifies promising regions of the feature space and passes them to SAA for local refinement. The resulting MVOSA-FS model was evaluated on ten high-dimensional benchmark datasets from the Arizona State University (ASU) repository. Support Vector Machine (SVM) classifiers were used to assess the classification accuracy. MVOSA-FS achieved superior performance compared to six state-of-the-art feature selection algorithms: Atom Search Optimization (ASO), Equilibrium Optimizer (EO), Emperor Penguin Optimizer (EPO), Monarch Butterfly Optimization (MBO), Satin Bowerbird Optimizer (SBO), and Sine Cosine Algorithm (SCA). Results: The proposed model yielded the lowest average classification error rate (1.45%), smallest standard deviation (0.008), and most compact feature subset (0.91%). The hybrid MVOSA-FS model effectively balances exploration and exploitation, delivering robust and scalable performance in feature selection for high-dimensional data. Conclusion: This hybridization approach demonstrates improved classification accuracy and reduced computational burden.
dc.identifier.citationMutlag, W. K., Mazher, W. J., Ibrahim, H. T., & Ucan, O. N. (2025). Boosting Multiverse Optimizer by Simulated Annealing for Dimensionality Reduction. Journal of Information Systems Engineering & Business Intelligence, 11(2), 254-266. 10.20473/jisebi.11.2.254-266
dc.identifier.doi10.20473/jisebi.11.2.254-266
dc.identifier.endpage266
dc.identifier.issn2598-6333
dc.identifier.issue2
dc.identifier.scopus2-s2.0-105012203922
dc.identifier.scopusqualityQ3
dc.identifier.startpage254
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5932
dc.identifier.volume11
dc.indekslendigikaynakScopus
dc.institutionauthorUçan, Osman Nuri
dc.publisherAirlangga University
dc.relation.ispartofJournal of Information Systems Engineering and Business Intelligence
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectFeature Selection
dc.subjectHigh-Dimensional Data
dc.subjectHybrid Optimization
dc.subjectMetaheuristics
dc.subjectMultiverse Optimizer
dc.subjectSimulated Annealing
dc.titleBoosting Multiverse Optimizer by Simulated Annealing for Dimensionality Reduction
dc.typeArticle

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