Boosting particle swarm optimization by backtracking search algorithm for optimization problems

Nama, S; Saha, AK; Chakraborty, S; Gandomi, AH; Abualigah, L

Boosting particle swarm optimization by backtracking search algorithm for optimization problems

Nama, S Saha, AK Chakraborty, S Gandomi, AH Abualigah, L

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Swarm and Evolutionary Computation, 2023, 79
Issue Date:: 2023-06-01

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Field	Value	Language
dc.contributor.author	Nama, S
dc.contributor.author	Saha, AK
dc.contributor.author	Chakraborty, S
dc.contributor.author	Gandomi, AH
dc.contributor.author	Abualigah, L
dc.date.accessioned	2024-04-02T03:48:10Z
dc.date.available	2024-04-02T03:48:10Z
dc.date.issued	2023-06-01
dc.identifier.citation	Swarm and Evolutionary Computation, 2023, 79
dc.identifier.issn	2210-6502
dc.identifier.issn	2210-6510
dc.identifier.uri	http://hdl.handle.net/10453/177427
dc.description.abstract	Adjusting the search behaviors of swarm-based algorithms during their execution is a fundamental errand for addressing real-world global optimizing challenges. Along this line, scholars are actively investigating the unvisited areas of a problem domain rationally. Particle Swarm Optimization (PSO), a popular swarm-based optimization algorithm, is broadly applied to resolve different real-world problems because of its more robust searching capacity. However, in some situations, due to an unbalanced trade-off between exploitation and exploration, PSO gets stuck in a suboptimal solution. To overcome this problem, this study proposes a new ensemble algorithm called e-mPSOBSA with the aid of the reformed Backtracking Search Algorithm (BSA) and PSO. The proposed technique first integrates PSO's operational potential and then introduces BSA's exploration capability to help boost global exploration, local exploitation, and an acceptable balance during the quest process. The IEEE CEC 2014 and CEC 2017 test function suite was considered for evaluation. The outcomes were contrasted with 26 state-of-the-art algorithms, including popular PSO and BSA variants. The convergence analysis, diversity analysis, and statistical test were also executed. In addition, the projected e-mPSOBSA was employed to evaluate four unconstrained and seven constrained engineering design problems, and performances were equated with various algorithms. All these analyses endorse the better performance of the suggested e-mPSOBSA for global optimization tasks, search performance, solution accuracy, and convergence rate.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Swarm and Evolutionary Computation
dc.relation.isbasedon	10.1016/j.swevo.2023.101304
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0802 Computation Theory and Mathematics
dc.subject.classification	4602 Artificial intelligence
dc.title	Boosting particle swarm optimization by backtracking search algorithm for optimization problems
dc.type	Journal Article
utslib.citation.volume	79
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0802 Computation Theory and Mathematics
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	2024-04-02T03:48:08Z
pubs.publication-status	Published
pubs.volume	79

Abstract:

Adjusting the search behaviors of swarm-based algorithms during their execution is a fundamental errand for addressing real-world global optimizing challenges. Along this line, scholars are actively investigating the unvisited areas of a problem domain rationally. Particle Swarm Optimization (PSO), a popular swarm-based optimization algorithm, is broadly applied to resolve different real-world problems because of its more robust searching capacity. However, in some situations, due to an unbalanced trade-off between exploitation and exploration, PSO gets stuck in a suboptimal solution. To overcome this problem, this study proposes a new ensemble algorithm called e-mPSOBSA with the aid of the reformed Backtracking Search Algorithm (BSA) and PSO. The proposed technique first integrates PSO's operational potential and then introduces BSA's exploration capability to help boost global exploration, local exploitation, and an acceptable balance during the quest process. The IEEE CEC 2014 and CEC 2017 test function suite was considered for evaluation. The outcomes were contrasted with 26 state-of-the-art algorithms, including popular PSO and BSA variants. The convergence analysis, diversity analysis, and statistical test were also executed. In addition, the projected e-mPSOBSA was employed to evaluate four unconstrained and seven constrained engineering design problems, and performances were equated with various algorithms. All these analyses endorse the better performance of the suggested e-mPSOBSA for global optimization tasks, search performance, solution accuracy, and convergence rate.

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http://hdl.handle.net/10453/177427