Mutation adaptive cuckoo search hybridized naked mole rat algorithm for industrial engineering problems.

Salgotra, R; Singh, S; Verma, P; Abualigah, L; Gandomi, AH

Mutation adaptive cuckoo search hybridized naked mole rat algorithm for industrial engineering problems.

Salgotra, R Singh, S Verma, P Abualigah, L Gandomi, AH

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Publisher:: NATURE PORTFOLIO
Publication Type:: Journal Article
Citation:: Sci Rep, 2025, 15, (1), pp. 19655
Issue Date:: 2025-06-04

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Field	Value	Language
dc.contributor.author	Salgotra, R
dc.contributor.author	Singh, S
dc.contributor.author	Verma, P
dc.contributor.author	Abualigah, L
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2025-07-15T06:50:35Z
dc.date.available	2025-05-02
dc.date.available	2025-07-15T06:50:35Z
dc.date.issued	2025-06-04
dc.identifier.citation	Sci Rep, 2025, 15, (1), pp. 19655
dc.identifier.issn	2045-2322
dc.identifier.issn	2045-2322
dc.identifier.uri	http://hdl.handle.net/10453/188402
dc.description.abstract	Cuckoo Search (CS) is a popular algorithm used to solve numerous challenging problems. In the present work, a novel variant of CS is presented to eliminate its shortcomings. The proposed algorithm is hybridized with the naked mole rat algorithm (NMRA) to enhance the exploitative behavior of CS, and is called Mutated Adaptive Cuckoo Search Algorithm (MaCN). This new algorithm has self-adaptive properties and its key feature is to divide the solutions into multiple sections, which are often called sub-swarms. In addition, a bare-bones search mechanism is also added to enhance exploration. The use of adaptive inertia weights helps optimize the switching probability, an important CS parameter that helps to achieve a balanced operation. The proposed MaCN algorithm is tested on CEC 2005 and CEC 2014 benchmark problems. Comparative studies showed that MaCN delivers promising results in solving CEC competition benchmark problems compared to JADE, success history-based adaptive DE (SHADE), LSHADE-SPACMA and self-adaptive DE (SaDE), among others. In addition to numerical benchmarks, MaCN is used to solve the industrial engineering frame structure and a comparison with hybridization of particle swarm with passive congregation (PSOPC), shuffled frog leaping algorithm hybrid with invasive weed optimization (SFLAIWO), particle swarm ant colony optimization (PSOACO), early strategy with DE (ES-DE), and others show its superiority. In addition, the Wilcoxon rankum and the Freidmann test statistically prove the significance of the proposed MaCN algorithm. MaCN was found to score first rank for the benchmarks. The application of the MaCN algorithm to solve the design problems of the suggests that the best new results are obtained for all test cases.
dc.format	Electronic
dc.language	eng
dc.publisher	NATURE PORTFOLIO
dc.relation.ispartof	Sci Rep
dc.relation.isbasedon	10.1038/s41598-025-01033-y
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Mutation adaptive cuckoo search hybridized naked mole rat algorithm for industrial engineering problems.
dc.type	Journal Article
utslib.citation.volume	15
utslib.location.activity	England
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	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-07-15T06:50:29Z
pubs.issue	1
pubs.publication-status	Published online
pubs.volume	15
utslib.citation.issue	1

Abstract:

Cuckoo Search (CS) is a popular algorithm used to solve numerous challenging problems. In the present work, a novel variant of CS is presented to eliminate its shortcomings. The proposed algorithm is hybridized with the naked mole rat algorithm (NMRA) to enhance the exploitative behavior of CS, and is called Mutated Adaptive Cuckoo Search Algorithm (MaCN). This new algorithm has self-adaptive properties and its key feature is to divide the solutions into multiple sections, which are often called sub-swarms. In addition, a bare-bones search mechanism is also added to enhance exploration. The use of adaptive inertia weights helps optimize the switching probability, an important CS parameter that helps to achieve a balanced operation. The proposed MaCN algorithm is tested on CEC 2005 and CEC 2014 benchmark problems. Comparative studies showed that MaCN delivers promising results in solving CEC competition benchmark problems compared to JADE, success history-based adaptive DE (SHADE), LSHADE-SPACMA and self-adaptive DE (SaDE), among others. In addition to numerical benchmarks, MaCN is used to solve the industrial engineering frame structure and a comparison with hybridization of particle swarm with passive congregation (PSOPC), shuffled frog leaping algorithm hybrid with invasive weed optimization (SFLAIWO), particle swarm ant colony optimization (PSOACO), early strategy with DE (ES-DE), and others show its superiority. In addition, the Wilcoxon rankum and the Freidmann test statistically prove the significance of the proposed MaCN algorithm. MaCN was found to score first rank for the benchmarks. The application of the MaCN algorithm to solve the design problems of the suggests that the best new results are obtained for all test cases.

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http://hdl.handle.net/10453/188402