Historical Information-Assisted Dynamic Response Integration and Adaptive Niche Methods for Dynamic Multimodal Optimization

Dynamic multimodal optimization problems (DMMOPs) represent the multimodal optimization problems that the optimal solution changes over time. Due to the wide application of DMMOPs in reality, some related algorithms have been proposed in recent years. Most existing algorithms employ a single dynamic response mechanism and embed it in existing multi-modal evolutionary algorithms. However, these algorithms often perform limited when environmental change involves multiple types, and they fail to consider utilizing historical information to assist static multimodal optimizers. To solve these issues, this paper proposes historical information-assisted dynamic response integration and adaptive niche methods (HIA-DRI-AN) for dynamic multi-modal optimization. In HIA-DRI-AN, an dynamic response integration method with adaptive adjustment mechanism is proposed for generating the initial population when the change happens. This method considers the change types of DMMOPs, and integrates targeted dynamic response mechanisms to respond to the different change types. Also, this method can adaptively self-adjust to balance the convergence and diversity of the initial population depending on the integrated response mechanism’s performance in historical environments. Furthermore, a niching adaptive division strategy is proposed to enhance the performance of the static optimizer. The strategy dynamically divides niches based on the integrated response mechanism’s performance and the current evolutionary stage, which can adjust the preference for diversity and convergence during evolution. The comprehensive experimental results on 24 test functions show that HIADRI-AN is superior compared to some state-of-the-art dynamic multimodal algorithms.