dc.contributor.author | Meraihi, Y | |
dc.contributor.author | Gabis, AB | |
dc.contributor.author | Mirjalili, S | |
dc.contributor.author | Ramdane-Cherif, A | |
dc.date.accessioned | 2021-04-13T04:47:16Z | |
dc.date.available | 2021-04-13T04:47:16Z | |
dc.date.issued | 2021 | |
dc.identifier.issn | 2169-3536 | |
dc.identifier.doi | 10.1109/ACCESS.2021.3067597 | |
dc.identifier.uri | http://hdl.handle.net/10072/403698 | |
dc.description.abstract | Grasshopper Optimization Algorithm (GOA) is a recent swarm intelligence algorithm inspired by the foraging and swarming behavior of grasshoppers in nature. The GOA algorithm has been successfully applied to solve various optimization problems in several domains and demonstrated its merits in the literature. This paper proposes a comprehensive review of GOA based on more than 120 scientific articles published by leading publishers: IEEE, Springer, Elsevier, IET, Hindawi, and others. It provides the GOA variants, including multi-objective and hybrid variants. It also discusses the main applications of GOA in various fields such as scheduling, economic dispatch, feature selection, load frequency control, distributed generation, wind energy system, and other engineering problems. Finally, the paper provides some possible future research directions in this area. | |
dc.description.peerreviewed | Yes | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | |
dc.relation.ispartofpagefrom | 50001 | |
dc.relation.ispartofpageto | 50024 | |
dc.relation.ispartofjournal | IEEE Access | |
dc.relation.ispartofvolume | 9 | |
dc.subject.fieldofresearch | Engineering | |
dc.subject.fieldofresearch | Information and computing sciences | |
dc.subject.fieldofresearchcode | 40 | |
dc.subject.fieldofresearchcode | 46 | |
dc.title | Grasshopper Optimization Algorithm: Theory, Variants, and Applications | |
dc.type | Journal article | |
dc.type.description | C1 - Articles | |
dcterms.bibliographicCitation | Meraihi, Y; Gabis, AB; Mirjalili, S; Ramdane-Cherif, A, Grasshopper Optimization Algorithm: Theory, Variants, and Applications, IEEE Access, 2021, 9, pp. 50001-50024 | |
dcterms.license | https://creativecommons.org/licenses/by/4.0/ | |
dc.date.updated | 2021-04-13T04:36:24Z | |
dc.description.version | Version of Record (VoR) | |
gro.rights.copyright | © The Author(s) 2021. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | |
gro.hasfulltext | Full Text | |
gro.griffith.author | Mirjalili, Seyedali | |