A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications

Laith Abualigah; Ali Diabat

doi:10.1007/s00521-020-04789-8

A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications

Laith Abualigah, Ali Diabat

Research output: Contribution to journal › Review article › peer-review

Abstract

The grasshopper optimization algorithm is one of the dominant modern meta-heuristic optimization algorithms. It has been successfully applied to various optimization problems in several fields, including engineering design, wireless networking, machine learning, image processing, control of power systems, and others. We survey the available literature on the grasshopper optimization algorithm, including its modifications, hybridizations, and generalization to the binary, chaotic, and multi-objective cases. We review its applications, evaluate the algorithms, and provide conclusions.

Original language	English (US)
Pages (from-to)	15533-15556
Number of pages	24
Journal	Neural Computing and Applications
Volume	32
Issue number	19
DOIs	https://doi.org/10.1007/s00521-020-04789-8
State	Published - Oct 1 2020

Keywords

Bio-inspired algorithms
Grasshopper optimization algorithm
Meta-heuristic optimization algorithms
Optimization problems

ASJC Scopus subject areas

Software
Artificial Intelligence

Access to Document

10.1007/s00521-020-04789-8

Cite this

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title = "A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications",

abstract = "The grasshopper optimization algorithm is one of the dominant modern meta-heuristic optimization algorithms. It has been successfully applied to various optimization problems in several fields, including engineering design, wireless networking, machine learning, image processing, control of power systems, and others. We survey the available literature on the grasshopper optimization algorithm, including its modifications, hybridizations, and generalization to the binary, chaotic, and multi-objective cases. We review its applications, evaluate the algorithms, and provide conclusions.",

keywords = "Bio-inspired algorithms, Grasshopper optimization algorithm, Meta-heuristic optimization algorithms, Optimization problems",

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