Abstract
The Kriging-based single-objective optimization for expensive black-box problems has been preventing the engineering application. The main challenge is how to reduce time consumption and improve convergence accuracy. To this end, a Kriging-based bi-objective constrained optimization (KBCO) algorithm is proposed. For each cycle, KBCO firstly uses the sampled design points to build or consecutively update Kriging models of expensive objective and constraint functions. And then, the predictive objective, root mean square error (RMSE) and maximum feasible probability produced by Kriging models are used to construct two objectives, which will be optimized by the NSGA-II solver to generate the Pareto optimal solutions. Finally, the Pareto front data will be further screened to obtain new expensive-evaluation sampling points and append them to sample data. Several numerical tests and a fuel economy simulation case for hydrogen fuel cell vehicle verify the feasibility and effectiveness of the KBCO method.
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