Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/7223
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dc.contributor.authorChatzis, Sotirios P.-
dc.contributor.authorKoukas, Spyros-
dc.contributor.otherΧατζής, Σωτήριος Π.-
dc.date.accessioned2013-02-19T15:22:12Zen
dc.date.accessioned2013-05-17T05:22:24Z-
dc.date.accessioned2015-12-02T10:01:45Z-
dc.date.available2013-02-19T15:22:12Zen
dc.date.available2013-05-17T05:22:24Z-
dc.date.available2015-12-02T10:01:45Z-
dc.date.issued2011-11-
dc.identifier.citationExpert systems with applications, 2011, vol. 38, no. 12, pp. 15332–15343en_US
dc.identifier.issn0957-4174-
dc.identifier.urihttp://ktisis.cut.ac.cy/handle/10488/7223-
dc.description.abstractThe bacterial foraging optimization (BFO) algorithm is a popular stochastic, population-based optimization technique that can be applied to a wide range of problems. Two are the major issues the BFO algorithm is confronted with: first, the foraging mechanism of BFO might in some cases induce the attraction of bacteria gathered near the global optimum by bacteria gathered to local optima, thus slowing down the whole population convergence. Second, BFO is susceptible to the curse-of-dimensionality, which makes it significantly harder to find the global optimum of a high-dimensional problem, compared to a low-dimensional problem with similar topology. In this paper, we introduce a novel BFO-based optimization algorithm aiming to address these issues, the synergetic bacterial swarming optimization (SBSO) algorithm. Our novel approach consists of: (i) the introduction of the swarming dynamics of the particle swarm optimization algorithm in the context of BFO, in order to ameliorate the convergence issues of the BFO bacteria foraging mechanism; and (ii) the utilization of multiple populations to optimize different components of the solution vector cooperatively, so as to mitigate the curse-of-dimensionality issues of the algorithm. We demonstrate the efficacy of our approach on several benchmark optimization problemsen_US
dc.language.isoenen_US
dc.publisherElsevieren
dc.relation.ispartofExpert systems with applicationsen_US
dc.rights© 2011 Elsevier. All rights reserveden_US
dc.subjectComputer scienceen_US
dc.subjectArtificial intelligenceen_US
dc.subjectExpert systems (Computer science)en_US
dc.subjectAlgorithmsen_US
dc.subjectBacteriaen_US
dc.subjectPopulation--Statisticsen_US
dc.subjectBacteriologyen_US
dc.titleNumerical optimization using synergetic swarms of foraging bacterial populationsen_US
dc.typeArticleen_US
dc.collaborationImperial College Londonen_US
dc.collaborationNational Technical University of Athens, Greeceen_US
dc.subject.categoryComputer and Information Sciencesen_US
dc.journalsHybrid Open Access Journalen_US
dc.countryUKen_US
dc.subject.fieldEngineering and Technologyen_US
dc.identifier.doi10.1016/j.eswa.2011.06.031en_US
dc.dept.handle123456789/54en
cut.common.academicyear2011-2012en_US
item.fulltextWith Fulltext-
item.grantfulltextopen-
item.languageiso639-1other-
crisitem.journal.journalissn0957-4174-
crisitem.journal.publisherElsevier-
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