A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization

We propose a surrogate-assisted reference vector guided evolutionary algorithm (EA) for computationally expensive optimization problems with more than three objectives. The proposed algorithm is based on a recently developed EA for many-objective optimization that relies on a set of adaptive referen...

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Vydané v:	IEEE transactions on evolutionary computation Ročník 22; číslo 1; s. 129 - 142
Hlavní autori:	Chugh, Tinkle, Jin, Yaochu, Miettinen, Kaisa, Hakanen, Jussi, Sindhya, Karthik
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 01.02.2018
Predmet:	Approximation algorithms Computational cost Computational modeling Convergence Evolutionary computation Kriging Linear programming model management multiobjective optimization Optimization reference vectors surrogate-assisted evolutionary algorithms (SAEAs) Training
ISSN:	1089-778X, 1941-0026
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