A Novel Metaheuristic Algorithm Inspired by Rhino Herd Behavior

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Wang, Gai-Ge
dc.contributor.author Gao, Xiaozhi
dc.contributor.author Zenger, Kai
dc.contributor.author Coelho, Leandro dos S.
dc.contributor.editor Juuso, Esko
dc.contributor.editor Dahlquist, Erik
dc.contributor.editor Leiviskä, Kauko
dc.date.accessioned 2019-02-25T08:45:40Z
dc.date.available 2019-02-25T08:45:40Z
dc.date.issued 2018
dc.identifier.citation Wang , G-G , Gao , X , Zenger , K & Coelho , L D S 2018 , A Novel Metaheuristic Algorithm Inspired by Rhino Herd Behavior . in E Juuso , E Dahlquist & K Leiviskä (eds) , Proceedings of The 9th EUROSIM Congress on Modelling and Simulation (EUROSIM 2016), The 57th SIMS Conference on Simulation and Modelling (SIMS 2016) . Linköping electronic conference proceedings , no. 142 , LINKÖPING UNIVERSITY ELECTRONIC PRESS , pp. 1026-1033 , EUROSIM Congress on Modelling and Simulation & SIMS Conference on Simulation and Modelling , Oulu , Finland , 12/09/2016 . https://doi.org/10.3384/ecp17142 en
dc.identifier.isbn 978-91-7685-399-3
dc.identifier.issn 1650-3686
dc.identifier.issn 1650-3740
dc.identifier.other PURE UUID: 4f062d1c-f517-45a7-a8e4-7e6087905603
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/a-novel-metaheuristic-algorithm-inspired-by-rhino-herd-behavior(4f062d1c-f517-45a7-a8e4-7e6087905603).html
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/31501650/ELEC_Wang_etal_A_Novel_Metaheuristic_Algorithm_EUROSIM_and_SIMS_2016.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/36739
dc.description.abstract In this paper paper, inspired by the herding behavior of rhinos, a new kind of swarm-based metaheuristic search method, namely Rhino Herd (RH), is proposed for solving global continuous optimization problems. In various studies of rhinos in nature, the synoptic model is used to describe rhino's space use and estimate its probability of occurrence within a given domain. The number of rhinos increases year by year, and this increment can be forecasted by several population size updating models. Synoptic model and a population size updating model are formalized and generalized to a general-purpose metaheuristic optimization algorithm. In RH, null model without introducing any influences is generated as the initial herding. This is followed by rhino modification via synoptic model. After that, the population size is updated by a certain population size updating model, and newly-generated rhinos are randomly initialized within the given conditions. RH is benchmarked by fifteen test problems in comparison with biogeography-based optimization (BBO) and stud genetic algorithm (SGA). The results clearly show the superiority of RH in searching for the better functi ark problems over BBO and SGA. en
dc.format.extent 8
dc.format.extent 1026-1033
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Linköping University Electronic Press
dc.relation.ispartof EUROSIM Congress on Modelling and Simulation & SIMS Conference on Simulation and Modelling en
dc.relation.ispartofseries Proceedings of The 9th EUROSIM Congress on Modelling and Simulation (EUROSIM 2016), The 57th SIMS Conference on Simulation and Modelling (SIMS 2016) en
dc.relation.ispartofseries Linköping electronic conference proceedings en
dc.relation.ispartofseries issue 142 en
dc.rights openAccess en
dc.subject.other 113 Computer and information sciences en
dc.title A Novel Metaheuristic Algorithm Inspired by Rhino Herd Behavior en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Jiangsu Normal University
dc.contributor.department Department of Electrical Engineering and Automation
dc.contributor.department Pontifical Catholic University of Parana
dc.contributor.department Department of Electrical Engineering and Automation en
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201902251896
dc.identifier.doi 10.3384/ecp17142
dc.type.version publishedVersion


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