Print Email Facebook Twitter Autoencoder enabled global optimization Title Autoencoder enabled global optimization Author Schumann, Julian (TU Delft Mechanical, Maritime and Materials Engineering) Contributor Aragon, A.M. (mentor) Toshniwal, D. (graduation committee) Bessa, M.A. (graduation committee) Degree granting institution Delft University of Technology Programme Mechanical Engineering | Precision and Microsystems Engineering Date 2021-09-08 Abstract High-dimensional optimization problems with expensive and non-convex cost functions pose a significant challenge, as the non-convexity limits the viability of local optimization, where the results are sensitive to initial guesses and often only represent local minima. But as the number of expensive cost function evaluations required for a full exploration of the search space grows exponentially with the increasing number of dimensions, the use of standard global optimization algorithms is also not practical. To overcome this obstacle and to lower the dimensionality of the problem, the use of an autoencoder for model order reduction is proposed. In the resulting lower dimensional space, standard global optimization methods can then be utilized, as fewer cost functions evaluations are necessary. For problems with comparatively more expensive cost functions, this optimization includes the employment of a surrogate model, which reduces the necessary number of these computationally expensive evaluations further. This proposed method is then tested firstly on a number of benchmark functions, where it shows the ability to find global optima under certain conditions. Secondly, the proposed method is used to solve a compliance minimization problem, where it shows the ability to improve upon a large number of designs generated by local optimization. Subject AutoencoderGlobal OptimizationTopology OptimizationDeflation To reference this document use: http://resolver.tudelft.nl/uuid:a7e5cdb8-c19f-4c67-bf4a-0d8f174f17ef Embargo date 2023-08-01 Part of collection Student theses Document type master thesis Rights © 2021 Julian Schumann Files PDF Thesis_5137586_.pdf 5.14 MB Close viewer /islandora/object/uuid:a7e5cdb8-c19f-4c67-bf4a-0d8f174f17ef/datastream/OBJ/view