Particle Swarm Optimization from Theory to Applications

Particle swarm optimization (PSO) is considered one of the most important methods in swarm intelligence.PSOisrelatedtothestudyofswarms;whereitisasimulationofbirdflocks.Itcanbe usedtosolveawidevarietyofoptimizationproblemssuchasunconstrainedoptimizationproblems, constrainedoptimizationproblems,nonlinearprogramming,multi-objectiveoptimization,stochastic programmingandcombinatorialoptimizationproblems.PSOhasbeenpresentedintheliterature andappliedsuccessfullyinreallifeapplications.Inthispaper,acomprehensivereviewofPSOas awell-knownpopulation-basedoptimizationtechnique.Thereviewstartsbyabriefintroductionto thebehaviorofthePSO,thenbasicconceptsanddevelopmentofPSOarediscussed,it’sfollowed bythediscussionofPSOinertiaweightandconstrictionfactoraswellasissuesrelatedtoparameter setting, selectionand tuning,dynamicenvironments, andhybridization.Also,we introduced the otherrepresentation,convergencepropertiesandtheapplicationsofPSO.Finally,conclusionsand discussionarepresented.Limitationstobeaddressedandthedirectionsofresearchinthefutureare identified,andanextensivebibliographyisalsoincluded.

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