Book chapter
Simulation-Based Design Optimization by Sequential Multi-criterion Adaptive Sampling and Dynamic Radial Basis Functions
Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences, pp.213-228
Computational Methods in Applied Sciences, Springer International Publishing
07/03/2018
DOI: 10.1007/978-3-319-89988-6_13
Abstract
The paper presents a global method for simulation-based design optimization (SBDO) which combines a dynamic radial basis function (DRBF) surrogate model with a sequential multi-criterion adaptive sampling (MCAS) technique. Starting from an initial training set, groups of new samples are sequentially selected aiming at both the improvement of the surrogate model global accuracy and the reduction of the objective function. The objective prediction and the associated uncertainty provided by the DRBF model are used by a multi-objective particle swarm optimization algorithm to identify Pareto-optimal solutions. These are used by the MCAS technique, which selects new samples by down-sampling the Pareto front, allowing for a parallel infill of an arbitrary number of points at each iteration. The method is applied to a set of 28 unconstrained global optimization test problems and a six-variable SBDO of the DTMB 5415 hull-form in calm water, based on potential flow simulations. Results show the effectiveness of the method in reducing the computational cost of the SBDO, providing the background for further developments and application to more complex ship hydrodynamic problems.
Details
- Title: Subtitle
- Simulation-Based Design Optimization by Sequential Multi-criterion Adaptive Sampling and Dynamic Radial Basis Functions
- Creators
- Matteo Diez - National Research CouncilSilvia Volpi - University of IowaAndrea Serani - National Research CouncilFrederick Stern - University of IowaEmilio F Campana - National Research Council
- Resource Type
- Book chapter
- Publication Details
- Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences, pp.213-228
- Publisher
- Springer International Publishing; Cham
- Series
- Computational Methods in Applied Sciences
- DOI
- 10.1007/978-3-319-89988-6_13
- ISSN
- 1871-3033
- Language
- English
- Date published
- 07/03/2018
- Academic Unit
- Mechanical Engineering
- Record Identifier
- 9984196503102771
Metrics
7 Record Views