2017年2月
Pareto frontier exploration in multiobjective topology optimization using adaptive weighting and point selection schemes
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
- ,
- ,
- ,
- 巻
- 55
- 号
- 2
- 開始ページ
- 409
- 終了ページ
- 422
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1007/s00158-016-1499-x
- 出版者・発行元
- SPRINGER
Topology optimization has been used in many industries and applied to a variety of design problems. In real-world engineering design problems, topology optimization problems often include a number of conflicting objective functions, such to achieve maximum stiffness and minimum mass of a design target. The existence of conflicting objective functions causes the results of the topology optimization problem to appear as a set of non-dominated solutions, called a Pareto-optimal solution set. Within such a solution set, a design engineer can easily choose the particular solution that best meets the needs of the design problem at hand. Pareto-optimal solution sets can provide useful insights that enable the structural features corresponding to a certain objective function to be isolated and explored. This paper proposes a new Pareto frontier exploration methodology for multiobjective topology optimization problems. In our methodology, a level set-based topology optimization method for a single-objective function is extended for use in multiobjective problems, using a population-based approach in which multiple points in the objective space are updated and moved to the Pareto frontier. The following two schemes are introduced so that Pareto-optimal solution sets can be efficiently obtained. First, weighting coefficients are adaptively determined considering the relative position of each point. Second, points in sparsely populated areas are selected and their neighborhoods are explored. Several numerical examples are provided to illustrate the effectiveness of the proposed method.
- リンク情報
- ID情報
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- DOI : 10.1007/s00158-016-1499-x
- ISSN : 1615-147X
- eISSN : 1615-1488
- Web of Science ID : WOS:000394987700002