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dc.contributor.authorNejlaoui, Mohamed-
dc.identifier.citationApplied and Computational Mechanics. 2024, vol. 18, no. 1, p. 77-110.en
dc.identifier.issn1802-680X (Print)
dc.identifier.issn2336-1182 (Online)
dc.format34 s.cs
dc.publisherUniversity of West Bohemiaen
dc.rightsUniversity of West Bohemia. All rights reserved.en
dc.subjectoptimalizační problémycs
dc.subjectalgoritmy učenícs
dc.subjectoptimalizační metodacs
dc.titleAn improved vibrating particles system method for many-criteria engineering design applicationsen
dc.description.abstract-translatedOptimization is getting more and more important due to its application in real engineering problems. Recently, the vibrating particles system algorithm has been developed as an efficient method for mono-objective optimization. However, in multi- and many-objective design problems, the vibrating particles system method is unable to handle simultaneously the conflicting objectives. The second drawback of the vibrating particles system algorithm is the~variability of the obtained results at each independent test, due to its inability to balance exploitation and exploration capabilities. To address these issues, this paper proposes an enhanced vibrating particles system algorithm called the many-objective vibrating particles system algorithm. The proposed many-objective vibrating particles system algorithm uses the Pareto principle to store the non-dominated solutions of multiple conflicting functions. Moreover, the implementation of the particle position enhancement mechanism to boost this algorithm's exploitation and exploration capabilities is another distinctive aspect of the suggested method. A variety of high-dimensional test functions and engineering design problems are used to evaluate the efficiency of the many-objective vibrating particles system algorithm. The obtained results show that the proposed algorithm outperforms other popular methods in terms of convergence characteristics and global search ability.en
dc.subject.translatedoptimization problemsen
dc.subject.translatedlearning algorithmsen
dc.subject.translatedoptimization methoden
dc.type.statusPeer revieweden
Appears in Collections:Volume 18, number 1 (2024)
Volume 18, number 1 (2024)

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Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/55656

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