Grasshopper Optimization Algorithm: Theory, Variants, and Applications
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 lit...
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doaj-3750d162cc9f4d3f8b9599c28cc273032021-04-05T17:38:43ZengIEEEIEEE Access2169-35362021-01-019500015002410.1109/ACCESS.2021.30675979381863Grasshopper Optimization Algorithm: Theory, Variants, and ApplicationsYassine Meraihi0https://orcid.org/0000-0002-3735-7797Asma Benmessaoud Gabis1https://orcid.org/0000-0003-3370-0030Seyedali Mirjalili2https://orcid.org/0000-0002-1443-9458Amar Ramdane-Cherif3https://orcid.org/0000-0001-8289-747XLaboratoire LIST, Université M’Hamed Bougara Boumerdes, Boumerdes, AlgeriaLaboratoire de Méthodes de Conception de Systèmes, Ecole Nationale Supérieure d’Informatique, Algiers, AlgeriaCentre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, QLD, AustraliaLISV Laboratory, University of Versailles St-Quentin-en-Yvelines, Velizy, FranceGrasshopper 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.https://ieeexplore.ieee.org/document/9381863/Grasshopper optimization algorithmGOAmeta-heuristicsoptimizationpopulation-based algorithmswarm intelligence |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yassine Meraihi Asma Benmessaoud Gabis Seyedali Mirjalili Amar Ramdane-Cherif |
spellingShingle |
Yassine Meraihi Asma Benmessaoud Gabis Seyedali Mirjalili Amar Ramdane-Cherif Grasshopper Optimization Algorithm: Theory, Variants, and Applications IEEE Access Grasshopper optimization algorithm GOA meta-heuristics optimization population-based algorithm swarm intelligence |
author_facet |
Yassine Meraihi Asma Benmessaoud Gabis Seyedali Mirjalili Amar Ramdane-Cherif |
author_sort |
Yassine Meraihi |
title |
Grasshopper Optimization Algorithm: Theory, Variants, and Applications |
title_short |
Grasshopper Optimization Algorithm: Theory, Variants, and Applications |
title_full |
Grasshopper Optimization Algorithm: Theory, Variants, and Applications |
title_fullStr |
Grasshopper Optimization Algorithm: Theory, Variants, and Applications |
title_full_unstemmed |
Grasshopper Optimization Algorithm: Theory, Variants, and Applications |
title_sort |
grasshopper optimization algorithm: theory, variants, and applications |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
description |
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. |
topic |
Grasshopper optimization algorithm GOA meta-heuristics optimization population-based algorithm swarm intelligence |
url |
https://ieeexplore.ieee.org/document/9381863/ |
work_keys_str_mv |
AT yassinemeraihi grasshopperoptimizationalgorithmtheoryvariantsandapplications AT asmabenmessaoudgabis grasshopperoptimizationalgorithmtheoryvariantsandapplications AT seyedalimirjalili grasshopperoptimizationalgorithmtheoryvariantsandapplications AT amarramdanecherif grasshopperoptimizationalgorithmtheoryvariantsandapplications |
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1721539142471385088 |