Enhanced Special Relativity Search Algorithm-Based Lorentz Force for Structural Optimization

Goodarzimehr, V; Fazli, M; Fanaie, N; Gandomi, AH

Enhanced Special Relativity Search Algorithm-Based Lorentz Force for Structural Optimization

Goodarzimehr, V Fazli, M Fanaie, N Gandomi, AH

Permalink

Publisher:: WORLD SCIENTIFIC PUBL CO PTE LTD
Publication Type:: Journal Article
Citation:: International Journal of Computational Methods, 2024
Issue Date:: 2024-01-01

Closed Access

	Filename	Description	Size
	24352856_17060427150005671.pdf	Accepted version	3.09 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Goodarzimehr, V
dc.contributor.author	Fazli, M
dc.contributor.author	Fanaie, N
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2025-04-04T10:02:55Z
dc.date.available	2025-04-04T10:02:55Z
dc.date.issued	2024-01-01
dc.identifier.citation	International Journal of Computational Methods, 2024
dc.identifier.issn	0219-8762
dc.identifier.issn	1793-6969
dc.identifier.uri	http://hdl.handle.net/10453/186640
dc.description.abstract	Optimization of real-world problems is an important challenge that researchers face due to the discrete nature of the search space, multiple local optimum, the large dimension of the problem, and computation costs. The Special Relativity Search (SRS) algorithm is one of the newest metaheuristic methods that has recently been developed. In this work, the Enhanced SRS (ESRS) algorithm has been developed based on the Lorentz Force coefficient to solve this class of problems. The SRS is a single-objective algorithm based on swarm intelligence inspired by the physics of special relativity. The cause of movement between particles in magnetic space is the Lorentz Force. Due to the type of particle charge, this force repels or attracts particles. SRS performance is sensitive to the Lorentz Force. Developing an empirical equation can enhance the performance of the algorithm. An empirical equation for simulating the Lorentz Force Coefficient is proposed as LC. LC decreases proportionally to the number of different iterations. This equation has a constant coefficient to control the movement step. This constant is more accurate for small values than for large values. Several structural problems with discrete and continuous variables have been investigated to evaluate the performance of the proposed algorithm. The objective function is the weight of structural elements that are under loading and must be reduced to the minimum value while observing the constraints of the problem. ESRS results have been compared with the original SRS and several state-of-the-art algorithms. The results show that ESRS has obtained the best optimal weight with the least computational effort and with high accuracy.
dc.language	English
dc.publisher	WORLD SCIENTIFIC PUBL CO PTE LTD
dc.relation.ispartof	International Journal of Computational Methods
dc.relation.isbasedon	10.1142/S0219876224500646
dc.rights	info:eu-repo/semantics/closedAccess
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	46 Information and computing sciences
dc.subject.classification	49 Mathematical sciences
dc.title	Enhanced Special Relativity Search Algorithm-Based Lorentz Force for Structural Optimization
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
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Data Science Institute (DSI)
utslib.copyright.status	closed_access	*
dc.date.updated	2025-04-04T10:02:53Z
pubs.publication-status	Published

Abstract:

Optimization of real-world problems is an important challenge that researchers face due to the discrete nature of the search space, multiple local optimum, the large dimension of the problem, and computation costs. The Special Relativity Search (SRS) algorithm is one of the newest metaheuristic methods that has recently been developed. In this work, the Enhanced SRS (ESRS) algorithm has been developed based on the Lorentz Force coefficient to solve this class of problems. The SRS is a single-objective algorithm based on swarm intelligence inspired by the physics of special relativity. The cause of movement between particles in magnetic space is the Lorentz Force. Due to the type of particle charge, this force repels or attracts particles. SRS performance is sensitive to the Lorentz Force. Developing an empirical equation can enhance the performance of the algorithm. An empirical equation for simulating the Lorentz Force Coefficient is proposed as LC. LC decreases proportionally to the number of different iterations. This equation has a constant coefficient to control the movement step. This constant is more accurate for small values than for large values. Several structural problems with discrete and continuous variables have been investigated to evaluate the performance of the proposed algorithm. The objective function is the weight of structural elements that are under loading and must be reduced to the minimum value while observing the constraints of the problem. ESRS results have been compared with the original SRS and several state-of-the-art algorithms. The results show that ESRS has obtained the best optimal weight with the least computational effort and with high accuracy.

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

http://hdl.handle.net/10453/186640