Steady state particle swarm
| dc.contributor.author | Fernandes, Carlos M. | |
| dc.contributor.author | Fachada, Nuno | |
| dc.contributor.author | Merelo, J. J. | |
| dc.contributor.author | Rosa, Agostinho C. | |
| dc.contributor.institution | Escola de Comunicação, Arquitetura, Artes e Tecnologias da Informação | |
| dc.date.issued | 2019 | |
| dc.description | PeerJ Computer Science | |
| dc.description.abstract | This paper investigates the performance and scalability of a new update strategy for the particle swarm optimization (PSO) algorithm. The strategy is inspired by the Bak–Sneppen model of co-evolution between interacting species, which is basically a network of fitness values (representing species) that change over time according to a simple rule: the least fit species and its neighbors are iteratively replaced with random values. Following these guidelines, a steady state and dynamic update strategy for PSO algorithms is proposed: only the least fit particle and its neighbors are updated and evaluated in each time-step; the remaining particles maintain the same position and fitness, unless they meet the update criterion. The steady state PSO was tested on a set of unimodal, multimodal, noisy and rotated benchmark functions, significantly improving the quality of results and convergence speed of the standard PSOs and more sophisticated PSOs with dynamic parameters and neighborhood. A sensitivity analysis of the parameters confirms the performance enhancement with different parameter settings and scalability tests show that the algorithm behavior is consistent throughout a substantial range of solution vector dimensions. | en |
| dc.description.sponsorship | Funding text 1 The following grant information was disclosed by the authors: Fundação para a Ciência e Tecnologia (FCT), Research Fellowship: SFRH/BPD/66876/2009. FCT PROJECT: UID/EEA/50009/2013. EPHEMECH: TIN2014-56494-C4-3-P, Spanish Ministry of Economy and Competitivity. PROY-PP2015-06: Plan Propio 2015 UGR. CEI2015-MP-V17 of the Microprojects program 2015 from CEI BioTIC Granada. Funding text 2 This work was supported by Fundação para a Ciência e Tecnologia (FCT) Research Fellowship SFRH/BPD/66876/2009 and FCT Project (UID/EEA/50009/2013), EPHEMECH (TIN2014-56494-C4-3-P, Spanish Ministry of Economy and Competitivity), PROY-PP2015-06 (Plan Propio 2015 UGR), project CEI2015-MP-V17 of the Microprojects program 2015 from CEI BioTIC Granada. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. | |
| dc.description.status | Non peer reviewed | |
| dc.format | application/pdf | |
| dc.identifier.citation | Fernandes , C M , Fachada , N , Merelo , J J & Rosa , A C 2019 , ' Steady state particle swarm ' , PeerJ Computer Science , vol. 2019 , no. 8 , e202 . https://doi.org/10.7717/peerj-cs.202 | |
| dc.identifier.doi | https://doi.org/10.7717/peerj-cs.202 | |
| dc.identifier.issn | 2376-5992 | |
| dc.identifier.url | https://www.scopus.com/record/display.uri?eid=2-s2.0-85074148237&origin=inward | |
| dc.language.iso | eng | |
| dc.publisher | PeerJ Inc. | |
| dc.relation.ispartofseries | vol.2019, no.8 | |
| dc.rights | openAccess | |
| dc.subject | MODELO BAK–SNEPPEN | |
| dc.subject | OTIMIZAÇÃO POR ENXAME DE PARTÍCULAS | |
| dc.subject | OTIMIZAÇÃO | |
| dc.subject | ALGORITMOS | |
| dc.subject | INTELIGÊNCIA ARTIFICIAL | |
| dc.subject | BAK–SNEPPEN MODEL | |
| dc.subject | PARTICLE SWARM OPTIMIZATION | |
| dc.subject | OPTIMIZATION | |
| dc.subject | ALGORITHMS | |
| dc.subject | ARTIFICIAL INTELLIGENCE | |
| dc.title | Steady state particle swarm | en |
| dc.type | article |