Dong, G, Liu, C, Liu, Y et al. (2 more authors) (2021) Computationally Efficient Approximations Using Adaptive Weighting Coefficients for Solving Structural Optimization Problems. Mathematical Problems in Engineering, 2021. 1743673. ISSN 1024-123X
Abstract
With rapid development of advanced manufacturing technologies and high demands for innovative lightweight constructions to mitigate the environmental and economic impacts, design optimization has attracted increasing attention in many engineering subjects, such as civil, structural, aerospace, automotive, and energy engineering. For nonconvex nonlinear constrained optimization problems with continuous variables, evaluations of the fitness and constraint functions by means of finite element simulations can be extremely expensive. To address this problem by algorithms with sufficient accuracy as well as less computational cost, an extended multipoint approximation method (EMAM) and an adaptive weighting-coefficient strategy are proposed to efficiently seek the optimum by the integration of metamodels with sequential quadratic programming (SQP). The developed EMAM stems from the principle of the polynomial approximation and assimilates the advantages of Taylor’s expansion for improving the suboptimal continuous solution. Results demonstrate the superiority of the proposed EMAM over other evolutionary algorithms (e.g., particle swarm optimization technique, firefly algorithm, genetic algorithm, metaheuristic methods, and other metamodeling techniques) in terms of the computational efficiency and accuracy by four well-established engineering problems. The developed EMAM reduces the number of simulations during the design phase and provides wealth of information for designers to effectively tailor the parameters for optimal solutions with computational efficiency in the simulation-based engineering optimization problems.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Guirong Dong et al.-is is an open access article distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Oct 2020 12:19 |
Last Modified: | 25 Jun 2023 22:27 |
Status: | Published |
Publisher: | Hindawi |
Identification Number: | 10.1155/2021/1743673 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:166291 |