Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/139039
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dc.contributor.authorZhou, Lingyunen_US
dc.contributor.authorDing, Lixinen_US
dc.contributor.authorMa, Maodeen_US
dc.contributor.authorTang, Wanen_US
dc.date.accessioned2020-05-15T02:30:55Z-
dc.date.available2020-05-15T02:30:55Z-
dc.date.issued2018-
dc.identifier.citationZhou, L., Ding, L., Ma, M., & Tang, W. (2019). An accurate partially attracted firefly algorithm. Computing, 101(5), 477-493. doi:10.1007/s00607-018-0645-2en_US
dc.identifier.issn0010-485Xen_US
dc.identifier.urihttps://hdl.handle.net/10356/139039-
dc.description.abstractThe firefly algorithm (FA) is a new and powerful algorithm for optimization. However, it has the disadvantages of high computational complexity and low convergence accuracy, especially when solving complex problems. In this paper, an accurate partially attracted firefly algorithm (PaFA) is proposed by adopting a partial attraction model and a fast attractiveness calculation strategy. The partial attraction model can preserve swarm diversity and make full use of individual information. The fast attractiveness calculation strategy ensures information sharing among the individuals and it also improves the convergence accuracy. The experimental results demonstrate the good performance of PaFA in terms of the solution accuracy compared with two state-of-the-art FA variants and two other bio-inspired algorithms.en_US
dc.language.isoenen_US
dc.relation.ispartofComputingen_US
dc.rights© 2018 Springer-Verlag GmbH Austria, part of Springer Nature. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleAn accurate partially attracted firefly algorithmen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doi10.1007/s00607-018-0645-2-
dc.identifier.scopus2-s2.0-85050256682-
dc.identifier.issue5en_US
dc.identifier.volume101en_US
dc.identifier.spage477en_US
dc.identifier.epage493en_US
dc.subject.keywordsFirefly Algorithmen_US
dc.subject.keywordsPartial Attraction Modelen_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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