Comparison of synchronous and asynchronous parallelization of extreme surrogate-assisted multi-objective evolutionary algorithm

This paper investigates the integration of a surrogate-assisted multi-objective evolutionary algorithm (MOEA) and a parallel computation scheme to reduce the computing time until obtaining the optimal solutions in evolutionary algorithms (EAs). A surrogate-assisted MOEA solves multi-objective optimi...

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Vydané v:	Natural computing Ročník 21; číslo 2; s. 187 - 217
Hlavní autori:	Harada, Tomohiro, Kaidan, Misaki, Thawonmas, Ruck
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Dordrecht Springer Netherlands 01.06.2022 Springer Nature B.V
Predmet:	Artificial Intelligence Artificial neural networks Complex Systems Computational efficiency Computer Science Computing time Evolutionary algorithms Evolutionary Biology Genetic algorithms Machine learning Multiple objective analysis Normal distribution Optimization Parallel processing Processor Architectures Theory of Computation Multi-objective optimization Extreme learning machine Surrogate model Parallelization Evolutionary computation
ISSN:	1567-7818, 1572-9796
On-line prístup:	Získať plný text
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