Surrogated-assisted multimodal multi-objective optimization for hybrid renewable energy system

Abstract Hybrid renewable energy system (HRES) is an effective tool to improve the utilization of renewable energy so as to enhance the quality of energy supply. The optimization of HRES includes a simulation process during a long time span, which is time-consuming. So far, introducing a surrogate model to replace the objective evaluation is an effective way to solve such problems. However, existing methods focused few on the diversity of solutions in the decision space. Based on this motivation, we proposed a novel surrogated-assisted multi-objective evolutionary algorithm that focuses on solving multimodal and time-expensive problems, termed SaMMEA. Specifically, we use a Gaussian process model to replace the calculation of the objective values. In addition, a special environmental selection strategy is proposed to enhance the diversity of solutions in the decision space and a model management method is proposed to better train the surrogate model. The proposed algorithm is then compared to several state-of-the-art algorithms on HRES problems, which indicates that the proposed algorithm is competitive.