A historical search-guided evolutionary framework for dynamic multiobjective optimization

In recent years, a number of dynamic multiobjective evolutionary algorithms (DMOEAs) have been proposed for tackling dynamic multiobjective optimization problems (DMOPs). Most of DMOEAs adopt learning methods to extract search experiences from past environments, trying to predict a promising initial population in new environments. However, they often ignore the use of search experiences to guide the evolutionary trajectory in new environments, which is also important to accelerate their convergence. Thus, this paper proposes a historical search-guided evolutionary (HSGE) framework for tackling DMOPs, which designs a neural network-based pattern learning (NNPL) strategy and a historical direction-guided evolutionary (HDGE) strategy. First, the NNPL strategy trains a neural network to effectively extract search experiences from historical environments. Then, based on these search experiences, the HDGE strategy is designed to steer the evolutionary direction of population, aiming to speed up its convergence in new environments. After embedding four dynamic response mechanisms into the HSGE framework, the corresponding DMOEAs have shown superior performance over the original DMOEAs on most test DMOPs. Moreover, the experimental results also validate the advantages of HSGE over two state-of-the-art optimization frameworks for tackling DMOPs. © 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.