Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/131107
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dc.contributor.authorAleksandar Lazinica
dc.date.accessioned2017-04-30T13:31:38Z-
dc.date.available2017-04-30T13:31:38Z-
dc.date.issued2009
dc.identifier.isbn978-953-7619-48-0
dc.identifier.urihttp://hdl.handle.net/123456789/131107-
dc.description.abstractParticle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field.
dc.language.isoeng
dc.publisherInTech
dc.relation.isbasedon10.5772/56679
dc.relation.urihttp://www.intechopen.com/books/particle_swarm_optimization
dc.rights.uriCC BY-NC-SA (姓名標示-非商業性-相同方式分享)
dc.sourceInTech
dc.subject.classificationComputer and Information Science
dc.subject.classification Numerical Analysis and Scientific Computing
dc.titleParticle Swarm Optimization
dc.type電子教課書
dc.classification自然科學類
Theme:教科書-自然科學類

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