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ToolsDong, Guirong, Liu, Chengyang, Liu, Dianzi and Mao, Xiaoan (2021) Adaptive multi-level search for global optimization: An integrated swarm intelligence-metamodelling technique. Applied Sciences, 11 (5). ISSN 2076-3417
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Abstract
Over the last decade, metaheuristic algorithms have emerged as a powerful paradigm for global optimization of multimodal functions formulated by nonlinear problems arising from various engineering subjects. However, numerical analyses of many complex engineering design problems may be performed using finite element method (FEM) or computational fluid dynamics (CFD), by which function evaluations of population-based algorithms are repetitively computed to seek a global optimum. It is noted that these simulations become computationally prohibitive for design optimization of complex structures. To efficiently and effectively address this class of problems, an adaptively integrated swarm intelligence-metamodelling (ASIM) technique enabling multi-level search and model management for the optimal solution is proposed in this paper. The developed technique comprises two steps: in the first step, a global-level exploration for near optimal solution is performed by adaptive swarm-intelligence algorithm, and in the second step, a local-level exploitation for the fine optimal solution is studied on adaptive metamodels, which are constructed by the multipoint approximation method (MAM). To demonstrate the superiority of the proposed technique over other methods, such as conventional MAM, particle swarm optimization, hybrid cuckoo search, and water cycle algorithm in terms of computational expense associated with solving complex optimization problems, one benchmark mathematical example and two real-world complex design problems are examined. In particular, the key factors responsible for the balance between exploration and exploitation are discussed as well.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | adaptive multi-level search,metamodel-based hybrid algorithm,multipoint approximation method,particle swarm optimization,materials science(all),instrumentation,engineering(all),process chemistry and technology,computer science applications,fluid flow and transfer processes ,/dk/atira/pure/subjectarea/asjc/2500 |
| Faculty \ School: | Faculty of Science > School of Engineering (former - to 2024) |
| UEA Research Groups: | Faculty of Science > Research Groups > Sustainable Energy Faculty of Science > Research Groups > Materials, Manufacturing & Process Modelling |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 26 Feb 2021 01:02 |
| Last Modified: | 07 Nov 2024 12:43 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/79338 |
| DOI: | 10.3390/app11052277 |
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