A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization

Evolutionary algorithms (EAs) have shown to be promising in solving many-objective optimization problems (MaOPs), where the performance of these algorithms heavily depends on whether solutions that can accelerate convergence toward the Pareto front and maintaining a high degree of diversity will be...

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Vydané v:	IEEE transactions on evolutionary computation Ročník 19; číslo 6; s. 761 - 776
Hlavní autori:	Zhang, Xingyi, Tian, Ye, Jin, Yaochu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.12.2015 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Convergence diversity Evolutionary computation Evolutionary multi-objective optimization hypervolume knee point many-objective optimization Measurement Optimization Pareto optimization Production scheduling Sociology evolutionary multiobjective optimization many-objective optimization diversity hypervolume (HV) knee point Convergence
ISSN:	1089-778X, 1941-0026
On-line prístup:	Získať plný text
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