Variational graph autoencoder-driven balancing strategy for multimodal multi-objective optimization

Multimodal multi-objective optimization aims to balance the diversity and the convergence to obtain multiple complete and uniform Pareto optimal solution sets. In recent years, using machine learning models to improve the performance of evolutionary algorithms has become a hot topic. However, few st...

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Veröffentlicht in:	Information sciences Jg. 712; S. 122116
Hauptverfasser:	Yang, Lei, Zhang, Erlei, Dang, Qianlong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Inc 01.09.2025
Schlagworte:	Dynamic niche Imbalance problem Inverted generation distance Multimodal multi-objective optimization Variational graph autoencoder Imbalance problem Inverted generation distance Dynamic niche Variational graph autoencoder Multimodal multi-objective optimization
ISSN:	0020-0255
Online-Zugang:	Volltext
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