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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Main Authors: Yassine Meraihi, Asma Benmessaoud Gabis, Seyedali Mirjalili, Amar Ramdane-Cherif
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
GOA
Online Access:https://ieeexplore.ieee.org/document/9381863/
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spelling 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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