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Particle swarm optimisation: a historical review up to the current developments

2020Journal article Open access
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dc.contributor.authorFreitas, Diogo
dc.contributor.authorLopes, Luiz Guerreiro
dc.contributor.authorDias, Fernando Morgado
dc.date.accessioned2021-10-20T14:05:13Z
dc.date.available2021-10-20T14:05:13Z
dc.date.issued2020
dc.description.abstractThe Particle Swarm Optimisation (PSO) algorithm was inspired by the social and biological behaviour of bird flocks searching for food sources. In this nature-based algorithm, individuals are referred to as particles and fly through the search space seeking for the global best position that minimises (or maximises) a given problem. Today, PSO is one of the most well-known and widely used swarm intelligence algorithms and metaheuristic techniques, because of its simplicity and ability to be used in a wide range of applications. However, in-depth studies of the algorithm have led to the detection and identification of a number of problems with it, especially convergence problems and performance issues. Consequently, a myriad of variants, enhancements and extensions to the original version of the algorithm, developed and introduced in the mid-1990s, have been proposed, especially in the last two decades. In this article, a systematic literature review about those variants and improvements is made, which also covers the hybridisation and parallelisation of the algorithm and its extensions to other classes of optimisation problems, taking into consideration the most important ones. These approaches and improvements are appropriately summarised, organised and presented, in order to allow and facilitate the identification of the most appropriate PSO variant for a particular application.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationFreitas, D., Lopes, L. G., & Morgado-Dias, F. (2020). Particle swarm optimisation: a historical review up to the current developments. Entropy, 22(3), 362. https://doi.org/10.3390/e22030362pt_PT
dc.identifier.doi10.3390/e22030362pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/3741
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectParticle Swarm Optimisation (PSO)pt_PT
dc.subjectSwarm intelligencept_PT
dc.subjectComputational intelligencept_PT
dc.subjectBio-inspired algorithmspt_PT
dc.subjectStochastic algorithmspt_PT
dc.subjectOptimisationpt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titleParticle swarm optimisation: a historical review up to the current developmentspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue3pt_PT
oaire.citation.startPage362pt_PT
oaire.citation.titleEntropypt_PT
oaire.citation.volume22pt_PT
person.familyNameTeixeira Freitas
person.familyNameGuerreiro Lopes
person.familyNameMorgado-Dias
person.givenNameDiogo Nuno
person.givenNameLuiz Carlos
person.givenNameFernando
person.identifieryfy16oUAAAAJ
person.identifierB-4961-2016
person.identifier.ciencia-id9C13-AF9C-25F3
person.identifier.ciencia-id4A18-1DCB-4862
person.identifier.ciencia-id7B14-DF07-AA6D
person.identifier.orcid0000-0002-2351-8676
person.identifier.orcid0000-0002-6145-8520
person.identifier.orcid0000-0001-7334-3993
person.identifier.scopus-author-id57205501523
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationb71e6dc9-523a-4300-92c3-4c459023a98c
relation.isAuthorOfPublicationce1b7737-282b-479c-b3a9-b7fcdae90250
relation.isAuthorOfPublication042f7593-c6ca-4553-8f0e-12ccf17018be
relation.isAuthorOfPublication.latestForDiscovery042f7593-c6ca-4553-8f0e-12ccf17018be

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