A Kriging model-based evolutionary algorithm with support vector machine for dynamic multimodal optimization

Dynamic multimodal optimization problems (DMMOPs) have to search multiple global optimal solutions with the objectives and constraints dynamically changing over time. In recent years, dynamic optimization problems and multimodal optimization problems have been extensively studied in the field of evolutionary computation. However, DMMOPs have not yet been paid significant attention and only a few studies have been designed for dynamic multimodal optimization. The key issue in optimizing DMMOPs is to address the challenges induced by both the multimodal nature and the dynamic nature. Existing works perform poorly in locating all global optima in static environments and tracking global optima with various change modes. Therefore, in this paper, a Kriging Model-based Evolutionary Algorithm with Support Vector Machine called KMEA-SVM is proposed for tackling DMMOPs. Two important operators are designed in this algorithm, including a Kriging-based preselection and a support vector machine (SVM)-based prediction. The aim of Kriging-based preselection is to search all global optimal solutions more efficiently by preselecting promising solutions with a trained Kriging model, while the purpose of SVM-based prediction is to predict more outstanding solutions as the initial population for new environment when the environment changes. The proposed KMEA-SVM is compared with several state-of-the-art evolutionary algorithms on twenty-four test DMMOPs and the experimental results validate the advantages of KMEA-SVM on seeking more multiple optima in dynamic environments.