A Contemporary Systematic Review on Meta-heuristic Optimization Algorithms with Their MATLAB and Python Code Reference

Salgotra, R; Sharma, P; Raju, S; gandomi, AH

A Contemporary Systematic Review on Meta-heuristic Optimization Algorithms with Their MATLAB and Python Code Reference

Salgotra, R Sharma, P Raju, S gandomi, AH

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Archives of Computational Methods in Engineering, 2023
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Salgotra, R
dc.contributor.author	Sharma, P
dc.contributor.author	Raju, S
dc.contributor.author	gandomi, AH
dc.date.accessioned	2024-02-18T23:41:11Z
dc.date.available	2024-02-18T23:41:11Z
dc.date.issued	2023-01-01
dc.identifier.citation	Archives of Computational Methods in Engineering, 2023
dc.identifier.issn	1134-3060
dc.identifier.issn	1886-1784
dc.identifier.uri	http://hdl.handle.net/10453/175728
dc.description.abstract	Optimization is a method which is used in every field, such as engineering, space, finance, fashion market, mass communication, travelling, and also in our daily activities. In every field, everyone always wants to minimize or maximize something called the objective function. Traditional and modern optimization techniques or Meta-Heuristic (MH) optimization techniques are used to solve the objective functions. But the traditional optimization techniques fail to solve the complex and real-world optimization problem consisting of non-linear objective functions. So many modern optimization techniques have been proposed exponentially over the last few decades to overcome these challenges. This paper discusses a brief review of the different benchmark test functions (BTFs) related to existing MH optimization algorithms (OA). It discusses the classification of MH algorithms reported in the literature regarding swarm-based, human-based, physics-based, and evolutionary-based methods. Based on the last half-century literature, MH-OAs are tabulated in terms of the proposed year, author, and inspiration agent. Furthermore, this paper presents the MATLAB and python code web-link of MH-OA. After reading this review article, readers will be able to use MH-OA to solve challenges in their field.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Archives of Computational Methods in Engineering
dc.relation.isbasedon	10.1007/s11831-023-10030-1
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Applied Mathematics
dc.subject.classification	40 Engineering
dc.subject.classification	49 Mathematical sciences
dc.title	A Contemporary Systematic Review on Meta-heuristic Optimization Algorithms with Their MATLAB and Python Code Reference
dc.type	Journal Article
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.date.updated	2024-02-18T23:41:10Z
pubs.publication-status	Published

Abstract:

Optimization is a method which is used in every field, such as engineering, space, finance, fashion market, mass communication, travelling, and also in our daily activities. In every field, everyone always wants to minimize or maximize something called the objective function. Traditional and modern optimization techniques or Meta-Heuristic (MH) optimization techniques are used to solve the objective functions. But the traditional optimization techniques fail to solve the complex and real-world optimization problem consisting of non-linear objective functions. So many modern optimization techniques have been proposed exponentially over the last few decades to overcome these challenges. This paper discusses a brief review of the different benchmark test functions (BTFs) related to existing MH optimization algorithms (OA). It discusses the classification of MH algorithms reported in the literature regarding swarm-based, human-based, physics-based, and evolutionary-based methods. Based on the last half-century literature, MH-OAs are tabulated in terms of the proposed year, author, and inspiration agent. Furthermore, this paper presents the MATLAB and python code web-link of MH-OA. After reading this review article, readers will be able to use MH-OA to solve challenges in their field.

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http://hdl.handle.net/10453/175728