A New Hypervolume-Based Evolutionary Algorithm for Many-Objective Optimization

In this article, a new hypervolume-based evolutionary multiobjective optimization algorithm (EMOA), namely, R2HCA-EMOA (R2-based hypervolume contribution approximation EMOA), is proposed for many-objective optimization. The core idea of the algorithm is to use an R2 indicator variant to approximate...

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Vydané v:	IEEE transactions on evolutionary computation Ročník 24; číslo 5; s. 839 - 852
Hlavní autori:	Shang, Ke, Ishibuchi, Hisao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.10.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Approximation algorithms Convergence Evolutionary algorithms hypervolume contribution approximation many-objective optimization Mathematical analysis Monte Carlo methods Multiple objective analysis Optimization Sociology Tensors
ISSN:	1089-778X, 1941-0026
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