Dynamic Constrained Multi-Objective Evolutionary Optimization via Adaptive Two-Stage Archiving and Autoencoder Prediction

Dynamic constrained multi-objective optimization problems (DCMOPs) are characterized by time-varying objectives and constraints, requiring optimization algorithms that can rapidly track the changing Pareto-Optimal Set (POS).A new dynamic constrained multi-objective evolutionary algorithm with adapti...

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Veröffentlicht in:	2023 5th International Conference on Data-driven Optimization of Complex Systems (DOCS) S. 1 - 8
Hauptverfasser:	Wang, Qianhui, Ye, Yulong, Zhu, Qingling, Liu, Songbai, Lin, Qiuzhen
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 22.09.2023
Schlagworte:	Constraint handling Constraint-handling techniques Denoising autoencoder Dynamic constrained multiobjective optimization Evolutionary computation Heuristic algorithms Noise reduction Prediction algorithms Sociology Transfer learning
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