History information-based Hill-Valley technique for multimodal optimization problems

Yu Li, Lingling Huang, Weifeng Gao, Zhifang Wei, Tianqi Huang, Jingwei Xu, Maoguo Gong

Abstract: The key of multimodal optimization algorithms is to divide the population into multiple species, and each species searches for a global optimum. This article proposes a new differential evolution based on the Hill-Valley technique for multimodal optimization. This technique uses history information to classify individuals on the same peak as one species. Compared to other niching techniques, this technique is insensitive to parameters and does not waste additional computing resources. For each species, the evolutionary state recognition method is developed to judge the evolutionary state and the search strategy is designed according to the evolutionary state. In addition, the prediction mechanism is designed to save computing resources. The experimental results show that the performance of the proposed algorithm is better than other algorithms on the CEC2013 test set and the NESs test set.