Adaptive surrogate modelling algorithm for meta-model-based design optimisation

M.N.P. Meibody, H. Naseh and F. Ommi

International Journal of Industrial and Systems Engineering, 2021, vol. 39, issue 3, 394-410

Abstract: In this paper, an adaptive meta-modelling algorithm is proposed for complex systems surrogate modelling. Progressive Latin hypercube sampling (PLHS) has been developed as the design of experiments (DOE) method for meta-modelling. In this DOE, the number of samples increases in an iterative process until the meta-modelling accuracy converges. To evaluate the effects of design parameters on the system response, sensitivity analysis has been performed. Particle swarm optimisation (PSO) algorithm is applied as the optimiser. The proposed methodology reduces the computational costs of the design optimisation process. The PLHS-based surrogate modelling is applied to the design of a space thruster nozzle as a case study. In this case, propulsion efficiency and mass (key factors of space propulsion systems) are considered as objective functions.

Keywords: surrogate modelling; progressive Latin hypercube sampling; PLHS; meta-model-based design optimisation; MBDO; Kriging; space nozzle. (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:39:y:2021:i:3:p:394-410

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