A novel evolutionary status guided hyper-heuristic algorithm for continuous optimization

This paper proposes a novel evolutionary status guided hyper-heuristic algorithm named ES-HHA for continuous optimization. A representative hyper-heuristic algorithm consists of two components: the low-level component and the high-level component. In the low-level component, to balance the exploitat...

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Veröffentlicht in:	Cluster computing Jg. 27; H. 9; S. 12209 - 12238
Hauptverfasser:	Zhong, Rui, Yu, Jun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.12.2024 Springer Nature B.V
Schlagworte:	Ablation Computer Communication Networks Computer Science Design Design optimization Distance learning Evolutionary algorithms Evolutionary computation Genetic algorithms Heuristic Heuristic methods Mutation Operating Systems Operators (mathematics) Optimization techniques Performance evaluation Processor Architectures Statistical analysis Task complexity Hyper-heuristic algorithm Evolutionary status Low-level heuristics (LLHs) Fitness distance correlation (FDC) Population diversity (PD)
ISSN:	1386-7857, 1573-7543
Online-Zugang:	Volltext
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