Chaotic-SCA Salp Swarm Algorithm Enhanced with Opposition Based Learning: Application to Decrease Carbon Footprint in Patient Flow

Vali, M; Salimifard, K; Gandomi, AH; Chaussalet, T

Chaotic-SCA Salp Swarm Algorithm Enhanced with Opposition Based Learning: Application to Decrease Carbon Footprint in Patient Flow

Vali, M Salimifard, K Gandomi, AH Chaussalet, T

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Publisher:: Springer
Publication Type:: Chapter
Citation:: Handbook of Nature-Inspired Optimization Algorithms: The State of the Art, 2022, 212, pp. 1-29
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Vali, M
dc.contributor.author	Salimifard, K
dc.contributor.author	Gandomi, AH
dc.contributor.author	Chaussalet, T
dc.date.accessioned	2023-02-12T23:47:12Z
dc.date.available	2023-02-12T23:47:12Z
dc.date.issued	2022-01-01
dc.identifier.citation	Handbook of Nature-Inspired Optimization Algorithms: The State of the Art, 2022, 212, pp. 1-29
dc.identifier.isbn	978-3-031-07511-7
dc.identifier.uri	http://hdl.handle.net/10453/166060
dc.description.abstract	Salp swarm algorithm is prone to problems such as a slow convergence rate and local optimal solution. To solve these problems, this chapter proposes the CSOSSA algorithm which integrates the sine cosine algorithm for updating the position of the leader, the chaotic maps are used for updating the position of followers, and opposition-based learning is used for a better exploration of the search space into the SSA. The CSOSSA is compared with the original SSA and other meta-heuristic algorithms on 13 benchmark functions of CEC2005 with unimodal or multimodal characteristics, and five De Jong's functions. The experimental results show that the performance of CSOSSA is better than or comparable with the SSA and other meta-heuristic algorithms. To show the applicability of the algorithm in real-world problems, then CSOSSA is applied to the green patient flow problem to minimize the total carbon emitted during the care process.
dc.format.extent	10
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Handbook of Nature-Inspired Optimization Algorithms: The State of the Art
dc.relation.ispartofseries	Studies in Systems, Decision and Control
dc.relation.isbasedon	10.1007/978-3-031-07512-4_1
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Chaotic-SCA Salp Swarm Algorithm Enhanced with Opposition Based Learning: Application to Decrease Carbon Footprint in Patient Flow
dc.type	Chapter
utslib.citation.volume	212
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	embargoed	*
pubs.consider-herdc	false
utslib.copyright.embargo	2024-09-01T00:00:00+1000Z
dc.date.updated	2023-02-12T23:47:10Z
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.volume	212
dc.location	Switzerland

Abstract:

Salp swarm algorithm is prone to problems such as a slow convergence rate and local optimal solution. To solve these problems, this chapter proposes the CSOSSA algorithm which integrates the sine cosine algorithm for updating the position of the leader, the chaotic maps are used for updating the position of followers, and opposition-based learning is used for a better exploration of the search space into the SSA. The CSOSSA is compared with the original SSA and other meta-heuristic algorithms on 13 benchmark functions of CEC2005 with unimodal or multimodal characteristics, and five De Jong's functions. The experimental results show that the performance of CSOSSA is better than or comparable with the SSA and other meta-heuristic algorithms. To show the applicability of the algorithm in real-world problems, then CSOSSA is applied to the green patient flow problem to minimize the total carbon emitted during the care process.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/166060