Publication:
Multi-Swarm bat algorithm

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dc.citedby9
dc.contributor.authorTaha A.M.en_US
dc.contributor.authorChen S.-D.en_US
dc.contributor.authorMustapha A.en_US
dc.contributor.authorid55699699200en_US
dc.contributor.authorid7410253413en_US
dc.contributor.authorid57200530694en_US
dc.date.accessioned2023-05-29T06:01:34Z
dc.date.available2023-05-29T06:01:34Z
dc.date.issued2015
dc.description.abstractIn this study a new Bat Algorithm (BA) based on multi-swarm technique called the Multi-Swarm Bat Algorithm (MSBA) is proposed to address the problem of premature convergence phenomenon. The problem happens when search process converges to non-optimal solution due to the loss of diversity during the evolution process. MSBA was designed with improved ability in exploring new solutions, which was essential in reducing premature convergence. The exploration ability was improved by having a number of sub-swarms watching over the best local optima. In MSBA, when the quality of best local optima does not improve after a pre-defined number of iterations, the population is split equally into several smaller sub-swarms, with one of them remains close to the current best local optima for further exploitation while the other sub-swarms continue to explore for new local optima. The proposed algorithm has been applied in feature selection problem and the results were compared against eight algorithms, which are Ant Colony Optimization (ACO), Genetic Algorithm (GA), Tabu Search (TS), Scatter Search (SS), Great Deluge Algorithm (GDA) and stander BA. The results showed that the MSBA is much more effective that it is able to find new best solutions at times when the rest of other algorithms are not able to. � Maxwell Scientific Organization, 2015.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.19026/rjaset.10.1839
dc.identifier.epage1395
dc.identifier.issue12
dc.identifier.scopus2-s2.0-84942038008
dc.identifier.spage1389
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84942038008&doi=10.19026%2frjaset.10.1839&partnerID=40&md5=f3f5f33089ca0262b5a7f73ba5eb9d2b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22520
dc.identifier.volume10
dc.publisherMaxwell Science Publicationsen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleResearch Journal of Applied Sciences, Engineering and Technology
dc.titleMulti-Swarm bat algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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