Multi-objective topology optimization incorporating an adaptive weighed-sum method and a configuration-based clustering scheme

A novel multi-objective topology optimization method is developed by simultaneously considering the diversity and uniformity of the optimum solutions in the objective and design variable spaces. To guarantee the diversity of the solutions, a configuration-based clustering scheme is developed and app...

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Vydáno v:Computer methods in applied mechanics and engineering Ročník 385; s. 114015
Hlavní autoři: Ryu, Namhee, Seo, Minsik, Min, Seungjae
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.11.2021
Elsevier BV
Témata:
Adaptive weight
Clustering
Clustering scheme
Compromise programming method
Configurations
Multi-objective optimization
Multiple objective analysis
Optimization
Pareto front
Pareto optimum
Topology optimization
Clustering scheme
Topology optimization
Compromise programming method
Pareto front
Multi-objective optimization
Adaptive weight
ISSN:0045-7825, 1879-2138
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Shrnutí:A novel multi-objective topology optimization method is developed by simultaneously considering the diversity and uniformity of the optimum solutions in the objective and design variable spaces. To guarantee the diversity of the solutions, a configuration-based clustering scheme is developed and applied to avoid similar designs. By clustering Pareto optimal designs during the optimization process, the searching region in the objective space is gradually reduced, and the time cost required for optimization can be decreased. Additionally, the uniformity of the solutions in the objective space is considered using an adaptive weight determination scheme. The results of the benchmark problems confirm that using the proposed method could reduce the time cost. Furthermore, the overall Pareto front and configuration of different designs are also explored. •Weights are systematically determined to obtain evenly distributed optimum solutions.•Reference point-based compromise programming is employed in the non-convex region of the Pareto front.•Clustering scheme is proposed to distinguish the Pareto optimal designs.•Adaptive weighted-sum and configuration-based clustering are incorporated in the optimization process.•The Pareto front can be explored while reducing the optimization process time cost.
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ISSN:0045-7825
1879-2138
DOI:10.1016/j.cma.2021.114015