A Temporal Graph Attention Network-Assisted Evolutionary Algorithm for Dynamic Constrained Multi-Objective Problems

Dynamic constrained multi-objective optimization problems involve time-varying objective functions and constraints and arise in many real-world engineering applications. However, existing algorithms often cannot rapidly identify the Pareto-optimal set and may converge slowly when the environment changes frequently. To address these challenges, this paper proposes a temporal graph attention network-assisted evolutionary algorithm, termed T-DCMOEA. T-DCMOEA constructs a temporal graph to represent the evolution of solution populations and uses an attention mechanism to capture temporal dependencies among solutions, thereby establishing an offline model for predicting solution quality. A Rényi divergence-based update-trigger indicator then determines whether the prediction model should be updated online. Finally, the resulting model guides the selection of promising solutions during environmental response, while an elite-preservation strategy generates additional candidates. Experimental results show that T-DCMOEA outperforms state-of-the-art algorithms across multiple performance metrics and produces solution sets with good convergence and diversity in dynamic environments.