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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Bibliographic Details
Published in:IEEE access Vol. 9; no. 7; pp. 50001 - 50024
Main Authors: Meraihi, Yassine, Gabis, Asma Benmessaoud, Mirjalili, Seyedali, Ramdane-Cherif, Amar
Format: Journal Article
Language:English
Published: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Computer Science
Distributed generation
Economics
Education
Frequency control
Genetic algorithms
GOA
Grasshopper optimization algorithm
meta-heuristics
MIMICs
Optimization
Optimization algorithms
Particle swarm optimization
population-based algorithm
Scientific papers
Stress concentration
Swarm intelligence
Swarming
Wind power
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary: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.
Bibliography:ObjectType-Article-1
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3067597