Multifidelity surrogates-assisted multi-objective particle swarm algorithm for offline data-driven optimization

Surrogate-assisted evolutionary algorithms have been widely employed to solve data-driven optimization problems. However, for offline data-driven optimization, it is very challenging to perform evolutionary search efficiently as well as accurately since no new data is available during the optimizati...

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Vydáno v:	Applied intelligence (Dordrecht, Netherlands) Ročník 54; číslo 22; s. 11649 - 11671
Hlavní autoři:	Cui, Yingying, Meng, Xi, Qiao, Junfei
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.11.2024 Springer Nature B.V
Témata:	Accuracy Archives & records Artificial Intelligence Computer Science Data search Evolutionary algorithms Genetic algorithms Knowledge management Machines Manufacturing Mechanical Engineering Multiple objective analysis Municipal waste management Optimization Particle swarm optimization Processes Solid waste management Solid wastes Waste disposal Two-archive multi-objective particle swarm optimization Offline data-driven optimization Multifidelity surrogate models Knowledge transfer Reference vectors
ISSN:	0924-669X, 1573-7497
On-line přístup:	Získat plný text
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