A Transformer-Based Spatiotemporal Perception Framework for Dynamic Constrained Multi-objective Optimization

Dynamic Constrained Multi-Objective Optimization Problems (DCMOPs) are widely encountered in real-world engineering applications, where both objective functions and constraints change over time, significantly increasing the complexity of problem-solving. Existing approaches still face limitations in historical information utilization, prediction accuracy of feasible solutions, and maintenance of population diversity—particularly in environment involving rapid and drastic shifts in feasible regions. In response to these challenges, this paper proposes a Transformer-Based Spatiotemporal Perception Framework (TSPF). By exploiting the Transformer’s strength in long-sequence modeling, the framework learns temporal and spatial dependencies from historical population to forecast future feasible and high-value regions. Furthermore, a memory-restart mechanism based on environmental similarity and a clustering-guided selection strategy are introduced to enhance the stability and diversity of the population. Experimental results on 18 benchmark problems with varying severities and frequencies of change and a real-world case study indicate that the proposed method achieves superior performance compared to several state-of-the-art algorithms, demonstrating its effectiveness and robustness in solving complex dynamic optimization problems.