Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems

Mirjalili, S; Gandomi, AH; Mirjalili, SZ; Saremi, S; Faris, H; Mirjalili, SM

Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems

Mirjalili, S Gandomi, AH Mirjalili, SZ Saremi, S Faris, H Mirjalili, SM

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Advances in Engineering Software, 2017, 114, pp. 163-191
Issue Date:: 2017-12-01

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Field	Value	Language
dc.contributor.author	Mirjalili, S
dc.contributor.author	Gandomi, AH
dc.contributor.author	Mirjalili, SZ
dc.contributor.author	Saremi, S
dc.contributor.author	Faris, H
dc.contributor.author	Mirjalili, SM
dc.date.accessioned	2022-08-25T01:39:09Z
dc.date.available	2022-08-25T01:39:09Z
dc.date.issued	2017-12-01
dc.identifier.citation	Advances in Engineering Software, 2017, 114, pp. 163-191
dc.identifier.issn	0965-9978
dc.identifier.issn	1873-5339
dc.identifier.uri	http://hdl.handle.net/10453/160814
dc.description.abstract	This work proposes two novel optimization algorithms called Salp Swarm Algorithm (SSA) and Multi-objective Salp Swarm Algorithm (MSSA) for solving optimization problems with single and multiple objectives. The main inspiration of SSA and MSSA is the swarming behaviour of salps when navigating and foraging in oceans. These two algorithms are tested on several mathematical optimization functions to observe and confirm their effective behaviours in finding the optimal solutions for optimization problems. The results on the mathematical functions show that the SSA algorithm is able to improve the initial random solutions effectively and converge towards the optimum. The results of MSSA show that this algorithm can approximate Pareto optimal solutions with high convergence and coverage. The paper also considers solving several challenging and computationally expensive engineering design problems (e.g. airfoil design and marine propeller design) using SSA and MSSA. The results of the real case studies demonstrate the merits of the algorithms proposed in solving real-world problems with difficult and unknown search spaces.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Advances in Engineering Software
dc.relation.isbasedon	10.1016/j.advengsoft.2017.07.002
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Applied Mathematics
dc.title	Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems
dc.type	Journal Article
utslib.citation.volume	114
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	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-08-25T01:39:07Z
pubs.publication-status	Published
pubs.volume	114

Abstract:

This work proposes two novel optimization algorithms called Salp Swarm Algorithm (SSA) and Multi-objective Salp Swarm Algorithm (MSSA) for solving optimization problems with single and multiple objectives. The main inspiration of SSA and MSSA is the swarming behaviour of salps when navigating and foraging in oceans. These two algorithms are tested on several mathematical optimization functions to observe and confirm their effective behaviours in finding the optimal solutions for optimization problems. The results on the mathematical functions show that the SSA algorithm is able to improve the initial random solutions effectively and converge towards the optimum. The results of MSSA show that this algorithm can approximate Pareto optimal solutions with high convergence and coverage. The paper also considers solving several challenging and computationally expensive engineering design problems (e.g. airfoil design and marine propeller design) using SSA and MSSA. The results of the real case studies demonstrate the merits of the algorithms proposed in solving real-world problems with difficult and unknown search spaces.

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http://hdl.handle.net/10453/160814