Spatiotemporal Knowledge-Driven Evolutionary Algorithm for Dynamic Constrained Multiobjective Optimization

Dynamic constrained multiobjective optimization problems (DCMOPs) present significant challenges due to timevarying objectives and constraints. To address these challenges, we propose a spatiotemporal knowledge-driven evolutionary algorithm (SKEA) that leverages both historical and current environme...

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Veröffentlicht in:	2025 7th International Conference on Data-driven Optimization of Complex Systems (DOCS) S. 271 - 276
Hauptverfasser:	Liu, Zhi, Song, Wei
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 19.08.2025
Schlagworte:	Current measurement differential evolution dynamic constrained multiobjective optimization environmental match Evolutionary computation Heuristic algorithms Knowledge transfer Neural networks Optimization Prediction algorithms Robustness Spatiotemporal phenomena Transfer learning
Online-Zugang:	Volltext
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