LAB: a leader–advocate–believer-based optimization algorithm

Reddy, R; Kulkarni, AJ; Krishnasamy, G; Shastri, AS; Gandomi, AH

LAB: a leader–advocate–believer-based optimization algorithm

Reddy, R Kulkarni, AJ Krishnasamy, G Shastri, AS Gandomi, AH

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Soft Computing, 2023, 27, (11), pp. 7209-7243
Issue Date:: 2023-06-01

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Field	Value	Language
dc.contributor.author	Reddy, R
dc.contributor.author	Kulkarni, AJ
dc.contributor.author	Krishnasamy, G
dc.contributor.author	Shastri, AS
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2024-04-29T02:08:03Z
dc.date.available	2024-04-29T02:08:03Z
dc.date.issued	2023-06-01
dc.identifier.citation	Soft Computing, 2023, 27, (11), pp. 7209-7243
dc.identifier.issn	1432-7643
dc.identifier.issn	1433-7479
dc.identifier.uri	http://hdl.handle.net/10453/178441
dc.description.abstract	This manuscript introduces a new socio-inspired metaheuristic technique referred to as leader–advocate–believer-based optimization algorithm (LAB) for engineering and global optimization problems. The proposed algorithm is inspired by the AI-based competitive behavior exhibited by the individuals in a group while simultaneously improving themselves and establishing a role (leader, advocate, believer). LAB performance in computational time and function evaluations are benchmarked using other metaheuristic algorithms. The algorithm is validated using the CEC 2005 and CEC 2017 benchmark functions. The algorithm was applied to solve engineering problems, including abrasive water jet machining, electric discharge machining, micro-machining processes and turning of titanium alloy in a minimum quantity lubrication environment. LAB algorithm was validated using the Friedman rank test. The results were compared with other algorithms such as FA, CI, GA, SA, PSO, Multi-CI, CMAES, ABC, SADE, CLPSO, BSA, IA, WOA, SHO, AVOA, LSHADE-Cn-EpsiN, FDB-SFS and LSHADE. For real-world problems, LAB outperformed SA, fbest and fbetter by achieving 76%, 85% and 75% minimization of Ra, respectively, for micro-milling with 0.7 mm tool diameter. For real-world problems, LAB achieved 81%, 72%, 85% minimization of Ra when compared to SA, fbest and fbetter for 1 mm tool diameter. LAB also achieved 24% and 34% minimization of Bh and Bt as compared to SA for micro-drilling with a tool diameter 0.5 mm. For tool diameters 0.8 mm and 0.9 mm, 16% and 3% minimization of Bt, respectively, were achieved as compared to SA. The results from this study highlighted that the LAB outperforms the other algorithms in terms of function evaluations and computational time. The prominent features and limitations of the algorithm are also discussed.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Soft Computing
dc.relation.isbasedon	10.1007/s00500-023-08033-y
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	46 Information and computing sciences
dc.title	LAB: a leader–advocate–believer-based optimization algorithm
dc.type	Journal Article
utslib.citation.volume	27
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	1702 Cognitive Sciences
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	2024-04-29T02:08:02Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	27
utslib.citation.issue	11

Abstract:

This manuscript introduces a new socio-inspired metaheuristic technique referred to as leader–advocate–believer-based optimization algorithm (LAB) for engineering and global optimization problems. The proposed algorithm is inspired by the AI-based competitive behavior exhibited by the individuals in a group while simultaneously improving themselves and establishing a role (leader, advocate, believer). LAB performance in computational time and function evaluations are benchmarked using other metaheuristic algorithms. The algorithm is validated using the CEC 2005 and CEC 2017 benchmark functions. The algorithm was applied to solve engineering problems, including abrasive water jet machining, electric discharge machining, micro-machining processes and turning of titanium alloy in a minimum quantity lubrication environment. LAB algorithm was validated using the Friedman rank test. The results were compared with other algorithms such as FA, CI, GA, SA, PSO, Multi-CI, CMAES, ABC, SADE, CLPSO, BSA, IA, WOA, SHO, AVOA, LSHADE-Cn-EpsiN, FDB-SFS and LSHADE. For real-world problems, LAB outperformed SA, fbest and fbetter by achieving 76%, 85% and 75% minimization of Ra, respectively, for micro-milling with 0.7 mm tool diameter. For real-world problems, LAB achieved 81%, 72%, 85% minimization of Ra when compared to SA, fbest and fbetter for 1 mm tool diameter. LAB also achieved 24% and 34% minimization of Bh and Bt as compared to SA for micro-drilling with a tool diameter 0.5 mm. For tool diameters 0.8 mm and 0.9 mm, 16% and 3% minimization of Bt, respectively, were achieved as compared to SA. The results from this study highlighted that the LAB outperforms the other algorithms in terms of function evaluations and computational time. The prominent features and limitations of the algorithm are also discussed.

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