A Derivative-Free Line-Search Algorithm for Simulation-Driven Design Optimization Using Multi-Fidelity Computations

Riccardo Pellegrini, Andrea Serani, Giampaolo Liuzzi, Francesco Rinaldi, Stefano Lucidi and Matteo Diez
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Riccardo Pellegrini: CNR-INM, National Research Council-Institute of Marine Engineering, 00128 Rome, Italy
Andrea Serani: CNR-INM, National Research Council-Institute of Marine Engineering, 00128 Rome, Italy
Giampaolo Liuzzi: Department of Computer, Control and Management Engineering “A. Ruberti”, Sapienza University, 00185 Rome, Italy
Francesco Rinaldi: Department of Mathematics, University of Padua, 35121 Padua, Italy
Stefano Lucidi: Department of Computer, Control and Management Engineering “A. Ruberti”, Sapienza University, 00185 Rome, Italy
Matteo Diez: CNR-INM, National Research Council-Institute of Marine Engineering, 00128 Rome, Italy

Mathematics, 2022, vol. 10, issue 3, 1-13

Abstract: The paper presents a multi-fidelity extension of a local line-search-based derivative-free algorithm for nonsmooth constrained optimization (MF-CS-DFN). The method is intended for use in the simulation-driven design optimization (SDDO) context, where multi-fidelity computations are used to evaluate the objective function. The proposed algorithm starts using low-fidelity evaluations and automatically switches to higher-fidelity evaluations based on the line-search step length. The multi-fidelity algorithm is driven by a suitably defined threshold and initialization values for the step length, which are associated to each fidelity level. These are selected to increase the accuracy of the objective evaluations while progressing to the optimal solution. The method is demonstrated for a multi-fidelity SDDO benchmark, namely pertaining to the hull-form optimization of a destroyer-type vessel, aiming at resistance minimization in calm water at fixed speed. Numerical simulations are based on a linear potential flow solver, where seven fidelity levels are used selecting systematically refined computational grids for the hull and the free surface. The method performance is assessed varying the steplength threshold and initialization approach. Specifically, four MF-CS-DFN setups are tested, and the optimization results are compared to its single-fidelity (high-fidelity-based) counterpart (CS-DFN). The MF-CS-DFN results are promising, achieving a resistance reduction of about 12% and showing a faster convergence than CS-DFN. Specifically, the MF extension is between one and two orders of magnitude faster than the original single-fidelity algorithm. For low computational budgets, MF-CS-DFN optimized designs exhibit a resistance that is about 6% lower than that achieved by CS-DFN.

Keywords: derivative-free; line search; multi-fidelity; simulation-driven design optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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