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A temporal graph attention network-assisted evolutionary algorithm for dynamic constrained multi-objective problems
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DOI：10.1016/j.knosys.2026.116892.png)
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Abstract
En 中文
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 R & eacute;nyi 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.
Keywords:
Dynamic constrained multi-objective
optimization
Graph attention network
Evolutionary algorithms
Environmental response
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K
IF:
7.6
Papers:
1.3W
Citations:
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