A multistrategy optimization improved artificial bee colony algorithm.
Being prone to the shortcomings of premature and slow convergence rate of artificial bee colony algorithm, an improved algorithm was proposed. Chaotic reverse learning strategies were used to initialize swarm in order to improve the global search ability of the algorithm and keep the diversity of th...
| Publicado en: | Scientific World Journal pp. 129483 - 129484 |
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| Formato: | research Journal Article |
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Wiley-Blackwell
2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103831753&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103831753 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 2014 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 103831753 103831753 NLM24982924 2012634753 10.1155/2014/129483 NLM24982924 PMC3997130 103831753 ppf: 129483 ppct: 1 formats: tig: atl: A multistrategy optimization improved artificial bee colony algorithm. aug: au: Liu, Wen affil: The School of Computer Science and Technology, Dalian University of Technology, Dalian, China ; Department of Electrical Engineering, Xinjiang Institute of Engineering, Tianjin Road, No. 176, Urumqi 830011, China. sug: subj: Algorithms Artificial Intelligence Bees and Wasps ab: Being prone to the shortcomings of premature and slow convergence rate of artificial bee colony algorithm, an improved algorithm was proposed. Chaotic reverse learning strategies were used to initialize swarm in order to improve the global search ability of the algorithm and keep the diversity of the algorithm; the similarity degree of individuals of the population was used to characterize the diversity of population; population diversity measure was set as an indicator to dynamically and adaptively adjust the nectar position; the premature and local convergence were avoided effectively; dual population search mechanism was introduced to the search stage of algorithm; the parallel search of dual population considerably improved the convergence rate. Through simulation experiments of 10 standard testing functions and compared with other algorithms, the results showed that the improved algorithm had faster convergence rate and the capacity of jumping out of local optimum faster. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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