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Categorical Structural Optimization: Methods and Applications

par Gao, Huanhuan
Président du jury Berke, Peter
Promoteur Massart, Thierry,Jacques ;Breitkopf, Piotr
Publication Non publié, 2018-12-07

Thèse de doctorat

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Résumé :

The thesis concentrates on a methodological research on categorical structural optimization by means of manifold learning. The main difficulty of handling the categorical optimization problems lies in the description of the design variables: they are presented in a discrete manner and do not have any orders. Thus the treatment of the design space is a key issue. In this thesis, the non-ordinal categorical variables are treated as multi-dimensional discrete variables, thus the dimensionality of corresponding design space becomes high. In order to reduce the dimensionality, the manifold learning techniques are introduced to find the intrinsic dimensionality and map the original design space to a reduced-order space. The mechanisms of both linear and non-linear manifold learning techniques are firstly studied. Then numerical examples are tested to compare the performance of manifold learning techniques. It is found that Principal Component Analysis (PCA) and Multi-dimensional Scaling (MDS) can only deal with linear or globally approximately linear cases. Isomap preserves the geodesic distances for non-linear manifold, however, its time consuming is the most. Locally Linear Embedding (LLE) preserves the neighbour weights and can yield good results in a short time. Kernel Principal Component Analysis (KPCA) works as a non-linear classifier and we proves the reason why it cannot preserve distances or angles in some cases.Based on the reduced-order representation obtained by Isomap, the graph-based evolutionary crossover and mutation operators are proposed to deal with categorical structural optimization problems, including the design of dome, six-story rigid frame and dame-like structures. The results show that the proposed graph-based evolutionary approach constructed on the reduced-order space performs more efficiently than traditional methods including simplex approach or evolutionary approach without reduced-order space.The Locally Linear Embedding is applied to reduce the data dimensionality and a polynomial interpolation helps to construct the responding surface from lower dimensional representation to original data. Then the continuous search method of moving asymptotes is executed and yields a competitively good but inadmissible solution within only a few of iteration numbers. Then in the second stage, a discrete search strategy is proposed to find out better solutions based on a neighbour search. The ten-bar truss and dome structural design problems are tested to show the validity of the method. In the end, this method is compared to the Simulated Annealing algorithm and Covariance Matrix Adaptation Evolutionary Strategy, showing its better optimization efficiency.In order to deal with the case in which the categorical design instances are distributed on several manifolds, we propose a k-manifolds learning method based on the Weighted Principal Component Analysis. The obtained manifolds are integrated in the lower dimensional design space. Then the two-stage search method is applied to solve the ten-bar truss, the dome and the dam-like structural design problems.