Bibliometric survey on particle swarm optimization algorithms (2001-2021)
| dc.authorid | 0000-0002-3452-1889 | en_US |
| dc.authorid | 0000-0002-6828-0112 | en_US |
| dc.contributor.author | Ajibade, Samuel-Soma M. | |
| dc.contributor.author | Ojeniyi, Adegoke | |
| dc.date.accessioned | 2023-03-09T10:32:13Z | |
| dc.date.available | 2023-03-09T10:32:13Z | |
| dc.date.issued | 2022 | en_US |
| dc.department | Fakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
| dc.description.abstract | Particle swarm optimization algorithms (PSOA) is a metaheuristic algorithm used to optimize computational problems using candidate solutions or particles based on selected quality measures. Despite the extensive research published, studies that critically examine its recent scientific developments and research impact are lacking. Therefore, the publication trends and research landscape on PSOA research were examined. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and bibliometric analysis techniques were applied to identify and analyze the published documents indexed in Scopus from 2001 to 2021. The published documents on PSOA increased from 8 to 1,717 (21,362.50%) due to the growing applications of PSOA in solving computational problems. "Conference papers" is the most common document type, whereas the most prolific researcher on PSOA is Andries P. Engelbrecht (South Africa). The most active affiliation (Ministry of Education) and funding organization (National Natural Science Foundation) are based in China. The research landscape on PSOA revealed high levels of publications, citations, and collaborations among the top authors, institutions, and countries worldwide. Keywords co-occurrence analysis revealed that "particle swarm optimization (PSO)" occurred more frequently than others. The findings of the study could provide researchers and policymakers with insights into the prospects and challenges of PSOA research relative to similar algorithms in the literature. | en_US |
| dc.identifier.doi | 10.1155/2022/3242949 | en_US |
| dc.identifier.uri | https://hdl.handle.net/11467/6409 | |
| dc.identifier.uri | https://doi.org/10.1155/2022/3242949 | |
| dc.identifier.wos | WOS:000890683500001 | en_US |
| dc.identifier.wosquality | N/A | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | HINDAWI LTD | en_US |
| dc.relation.ispartof | JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Differential evolution, global optimization | en_US |
| dc.title | Bibliometric survey on particle swarm optimization algorithms (2001-2021) | en_US |
| dc.type | Article | en_US |
