A dual objective global optimization algorithm based on adaptive weighted hybrid surrogate model for the hydrogen fuel utilization in hydrogen fuel cell vehicle

Current engineering optimizations mainly use surrogate models (SMs) to approximate complex black-box problems. However, SMs with different approximate characteristics may make the designers unable to accurately judge which type of SMs is more suitable for the actual optimization design. A reasonable combination of different SMs might be one of the solutions. To this end, a global optimization algorithm based on an adaptive weighted hybrid surrogate model (GOA-AWHS) is proposed. In each iteration, a hybrid model based on kriging and RBF is first constructed by adaptively selecting weight coefficients. Next, two objectives consisting of predicted objective, root mean square error and distance parameters are optimized to generate the Pareto frontier. Finally, further selection of data points on the Pareto front yields multiple promising optimal solutions. A series of standard numerical functions and hydrogen fuel utilization in hydrogen fuel cell vehicles are tested to demonstrate the effectiveness and robustness of the GOA-AWHS method. • A global optimization method based on hybrid surrogate model is proposed. • The hybrid model is built by adaptively selecting the weights of Kriging and RBF. • Two objectives are constructed and used to improve optimization quality. • The comparison results with other methods show its superiority. • A simulation case of hydrogen fuel utilization is validated.