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An adaptive gravitational search algorithm for global optimization

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dc.citedby1
dc.contributor.authorKoay Y.-Y.en_US
dc.contributor.authorTan J.-D.en_US
dc.contributor.authorLim C.-W.en_US
dc.contributor.authorKoh S.-P.en_US
dc.contributor.authorTiong S.-K.en_US
dc.contributor.authorAli K.en_US
dc.contributor.authorid57189626122en_US
dc.contributor.authorid38863172300en_US
dc.contributor.authorid35722335000en_US
dc.contributor.authorid22951210700en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid36130958600en_US
dc.date.accessioned2023-05-29T07:29:13Z
dc.date.available2023-05-29T07:29:13Z
dc.date.issued2019
dc.description.abstractOptimization algorithm has become one of the most studied branches in the fields of artificial intelligent and soft computing. Many powerful optimization algorithms with global search ability can be found in the literature. Gravitational Search Algorithm (GSA) is one of the relatively new population-based optimization algorithms. In this research, an Adaptive Gravitational Search Algorithm (AGSA) is proposed. The AGSA is enhanced with an adaptive search step local search mechanism. The adaptive search step begins the search with relatively larger step size, and automatically fine-tunes the step size as iterations go. This enhancement grants the algorithm a more powerful exploitation ability, which in turn grants solutions with higher accuracies. The proposed AGSA was tested in a test suit with several well-established optimization test functions. The results showed that the proposed AGSA out-performed other algorithms such as conventional GSA and Genetic Algorithm in the benchmarking of speed and accuracy. It can thus be concluded that the proposed AGSA performs well in solving local and global optimization problems. Applications of the AGSA to solve practical engineering optimization problems can be considered in the future. Copyright � 2019 Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/ijeecs.v16.i2.pp724-729
dc.identifier.epage729
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85073562079
dc.identifier.spage724
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85073562079&doi=10.11591%2fijeecs.v16.i2.pp724-729&partnerID=40&md5=6fcd801280feab4ec889a8db61b2c1c6
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24941
dc.identifier.volume16
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access, Hybrid Gold
dc.sourceScopus
dc.sourcetitleIndonesian Journal of Electrical Engineering and Computer Science
dc.titleAn adaptive gravitational search algorithm for global optimizationen_US
dc.typeArticleen_US
dspace.entity.typePublication
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