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Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12128/4166
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dc.contributor.authorGościniak, Ireneusz-
dc.date.accessioned2018-06-04T06:13:04Z-
dc.date.available2018-06-04T06:13:04Z-
dc.date.issued2015-
dc.identifier.citationExpert Systems with Applications, Vol. 42, iss. 2 (2015), s. 844-854pl_PL
dc.identifier.issn0957-4174-
dc.identifier.urihttp://hdl.handle.net/20.500.12128/4166-
dc.description.abstractParticularly interesting group consists of algorithms that implement co-evolution or co-operation in natural environments, giving much more powerful implementations. The main aim is to obtain the algorithm which operation is not influenced by the environment. An unusual look at optimization algorithms made it possible to develop a new algorithm and its metaphors define for two groups of algorithms. These studies concern the particle swarm optimization algorithm as a model of predator and prey. New properties of the algorithm resulting from the co-operation mechanism that determines the operation of algorithm and significantly reduces environmental influence have been shown. Definitions of functions of behavior scenarios give new feature of the algorithm. This feature allows self controlling the optimization process. This approach can be successfully used in computer games. Properties of the new algorithm make it worth of interest, practical application and further research on its development. This study can be also an inspiration to search other solutions that implementing co-operation or co-evolution.pl_PL
dc.language.isoenpl_PL
dc.rightsUznanie autorstwa-Użycie niekomercyjne-Bez utworów zależnych 3.0 Polska*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/pl/*
dc.subjectCo-evolutionary systemspl_PL
dc.subjectPSO algorithmpl_PL
dc.subjectPredator–prey algorithmpl_PL
dc.subjectImmune algorithmpl_PL
dc.subjectOptimization methodpl_PL
dc.subjectGamespl_PL
dc.subjectArtificial intelligencepl_PL
dc.subjectEntropypl_PL
dc.subjectMultifractal analysispl_PL
dc.titleA new approach to particle swarm optimization algorithmpl_PL
dc.typeinfo:eu-repo/semantics/articlepl_PL
dc.relation.journalExpert Systems with Applicationspl_PL
dc.identifier.doi10.1016/j.eswa.2014.07.034-
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