Improving Approaches for Meta-heuristic Algorithms: A Brief Overview

Yaghoubzadeh-Bavandpour, A; Bozorg-Haddad, O; Zolghadr-Asli, B; Gandomi, AH

Improving Approaches for Meta-heuristic Algorithms: A Brief Overview

Yaghoubzadeh-Bavandpour, A Bozorg-Haddad, O Zolghadr-Asli, B Gandomi, AH

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Publisher:: Springer Nature
Publication Type:: Chapter
Citation:: Studies in Computational Intelligence, 2022, 1043, pp. 35-61
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Yaghoubzadeh-Bavandpour, A
dc.contributor.author	Bozorg-Haddad, O
dc.contributor.author	Zolghadr-Asli, B
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2023-03-04T21:17:10Z
dc.date.available	2023-03-04T21:17:10Z
dc.date.issued	2022-01-01
dc.identifier.citation	Studies in Computational Intelligence, 2022, 1043, pp. 35-61
dc.identifier.isbn	9789811925184
dc.identifier.uri	http://hdl.handle.net/10453/166660
dc.description.abstract	Optimization problems can be observed in many applications, ranging from engineering applications and decision-making to computer science and finance. Optimization can be described as a process for discovering optimal solution among all available solutions of the defined problem, considering complex and high-dimensional constraints in searching for the optimal solution. Among the numerous approaches for solving optimization issues, optimization algorithms are one of the most well-known methods. Such algorithms possess the ability and efficiency to solve optimization issues in different science and engineering disciplines; however, these algorithms face problems, such as slow convergence speed and falling into local optimum. Various methods can be applied to improve optimization algorithms’ performance. This chapter aims to introduce some of these methods and their theory, along with different combination methods to achieve better performance in finding the global solution.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Studies in Computational Intelligence
dc.relation.ispartofseries	Studies in Computational Intelligence
dc.relation.isbasedon	10.1007/978-981-19-2519-1_2
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Improving Approaches for Meta-heuristic Algorithms: A Brief Overview
dc.type	Chapter
utslib.citation.volume	1043
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-04T21:17:09Z
pubs.publication-status	Published
pubs.volume	1043

Abstract:

Optimization problems can be observed in many applications, ranging from engineering applications and decision-making to computer science and finance. Optimization can be described as a process for discovering optimal solution among all available solutions of the defined problem, considering complex and high-dimensional constraints in searching for the optimal solution. Among the numerous approaches for solving optimization issues, optimization algorithms are one of the most well-known methods. Such algorithms possess the ability and efficiency to solve optimization issues in different science and engineering disciplines; however, these algorithms face problems, such as slow convergence speed and falling into local optimum. Various methods can be applied to improve optimization algorithms’ performance. This chapter aims to introduce some of these methods and their theory, along with different combination methods to achieve better performance in finding the global solution.

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http://hdl.handle.net/10453/166660