A Parallel Multiobjective Efficient Global Optimization: The Finite Element Method in Optimal Design and Model Development (original) (raw)
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A computationally efficient surrogate model is suggested to approximate the objective and constraint function values, which replace expensive evaluation of the objective and constraint function values in numerical simulation-based optimization. Kriging surrogate model has been widely used in surrogate-based design optimization (SBDO) to replace the highly nonlinear black-box functions. In this paper, a novel adaptive Kriging model based on parallel infilling strategy is proposed to improve both the numerical accuracy and efficiency of the SBDO methods. The parallel infilling strategy consists of two parts: local sampling and globaluthor sampling. In the local sampling, new additional sampling points are generated only within a limited region that is determined according to the optimal point at the last iteration, while in global sampling they are generated based on the fitting error estimation in the whole region. The effectiveness of the proposed algorithm is verified through applications to analytical functions. Then the algorithm is applied to the multi-objective optimal design of an ironless permanent magnet synchronous linear motor.
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