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A bi-level methodology for solving large-scale mixed categorical structural optimization

Abstract : In this work, large-scale structural optimization problems involving non-ordinal categorical design variables and continuous variables are investigated. The aim is to minimize the weight of a structure with respect to cross-section areas, with materials and stiffening principles selection. First, the problem is formulated using a bi-level decomposition involving master and slave problems. The master problem is given by a first-order-like approximation that helps to drastically reduce the combinatorial explosion raised by the categorical variables. Continuous variables are handled in a slave problem solved using a gradient-based approach, where the categorical variables are driven by the master problem. The proposed algorithm is tested on three different structural optimization test cases. A comparison to state-of-the-art algorithms emphasize efficiency of the proposed algorithm in terms of the optimum quality, the computation cost, and the scaling with respect to the problem dimension.
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Submitted on : Thursday, April 9, 2020 - 11:53:08 AM
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Pierre-Jean Barjhoux, Youssef Diouane, Stéphane Grihon, Dimitri Bettebghor, Joseph Morlier. A bi-level methodology for solving large-scale mixed categorical structural optimization. Structural and Multidisciplinary Optimization, Springer Verlag (Germany), 2020, 62, pp.337-351. ⟨10.1007/s00158-020-02491-w⟩. ⟨hal-02538198⟩



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