Adaptive Strategy Management: A new framework for large-scale structural optimization design

Talatahari, S; Nouhi, B; Beheshti, A; Chen, F; Gandomi, AH

Adaptive Strategy Management: A new framework for large-scale structural optimization design

Talatahari, S Nouhi, B Beheshti, A Chen, F

Gandomi, AH

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Publisher:: ELSEVIER SCIENCE SA
Publication Type:: Journal Article
Citation:: Computer Methods in Applied Mechanics and Engineering, 2025, 446
Issue Date:: 2025-11-01

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Field	Value	Language
dc.contributor.author	Talatahari, S
dc.contributor.author	Nouhi, B
dc.contributor.author	Beheshti, A
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2025-11-21T06:22:01Z
dc.date.available	2025-11-21T06:22:01Z
dc.date.issued	2025-11-01
dc.identifier.citation	Computer Methods in Applied Mechanics and Engineering, 2025, 446
dc.identifier.issn	0045-7825
dc.identifier.issn	1879-2138
dc.identifier.uri	http://hdl.handle.net/10453/190755
dc.description.abstract	This study introduces the Adaptive Strategy Management (ASM) framework designed to enhance the efficiency of computationally expensive optimization processes by dynamically switching between multiple solution-generation strategies. The ASM framework integrates three core steps: filtering, switching, and updating, which allow it to adaptively decide which solutions to evaluate based on real-time performance feedback. Several ASM-based variants are proposed, each implementing different filtering and switching mechanisms, such as generated-based selection, proximity-based filtering, and strategy switching guided by the current or global best solutions. Chaos Game Optimization (CGO) is selected as the core optimizer, with its updated equations modified to improve performance without incurring additional computational costs alongside strategy-level innovations. Extensive evaluations on medium-, large- and very large-scale structural problems demonstrate that the developed methods consistently outperform other approaches. Notably, the ASM-Close Global Best method, which combines proximity filtering with global best knowledge, achieved superior results across all performance intervals, showcasing robust convergence and high-quality solutions. These findings underscore the potential of Adaptive Strategy Management in improving large-scale optimization performance and open new directions for future research, including other strategy selections, broader applications across metaheuristics, and extensions to multi-objective and constrained optimization problems.
dc.language	English
dc.publisher	ELSEVIER SCIENCE SA
dc.relation.ispartof	Computer Methods in Applied Mechanics and Engineering
dc.relation.isbasedon	10.1016/j.cma.2025.118256
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	01 Mathematical Sciences, 09 Engineering
dc.subject.classification	Applied Mathematics
dc.subject.classification	40 Engineering
dc.subject.classification	49 Mathematical sciences
dc.title	Adaptive Strategy Management: A new framework for large-scale structural optimization design
dc.type	Journal Article
utslib.citation.volume	446
utslib.for	01 Mathematical 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)
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
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	2025-11-21T06:21:57Z
pubs.publication-status	Published
pubs.volume	446

Abstract:

This study introduces the Adaptive Strategy Management (ASM) framework designed to enhance the efficiency of computationally expensive optimization processes by dynamically switching between multiple solution-generation strategies. The ASM framework integrates three core steps: filtering, switching, and updating, which allow it to adaptively decide which solutions to evaluate based on real-time performance feedback. Several ASM-based variants are proposed, each implementing different filtering and switching mechanisms, such as generated-based selection, proximity-based filtering, and strategy switching guided by the current or global best solutions. Chaos Game Optimization (CGO) is selected as the core optimizer, with its updated equations modified to improve performance without incurring additional computational costs alongside strategy-level innovations. Extensive evaluations on medium-, large- and very large-scale structural problems demonstrate that the developed methods consistently outperform other approaches. Notably, the ASM-Close Global Best method, which combines proximity filtering with global best knowledge, achieved superior results across all performance intervals, showcasing robust convergence and high-quality solutions. These findings underscore the potential of Adaptive Strategy Management in improving large-scale optimization performance and open new directions for future research, including other strategy selections, broader applications across metaheuristics, and extensions to multi-objective and constrained optimization problems.

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http://hdl.handle.net/10453/190755