A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization

In evolutionary multiobjective optimization, maintaining a good balance between convergence and diversity is particularly crucial to the performance of the evolutionary algorithms (EAs). In addition, it becomes increasingly important to incorporate user preferences because it will be less likely to...

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Vydané v:	IEEE transactions on evolutionary computation Ročník 20; číslo 5; s. 773 - 791
Hlavní autori:	Ran Cheng, Yaochu Jin, Olhofer, Markus, Sendhoff, Bernhard
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 01.10.2016
Predmet:	Angle-penalized distance (APD) Convergence diversity Evolutionary computation evolutionary multiobjective optimization Linear programming many-objective optimization Pareto optimization preference articulation reference vector Sociology
ISSN:	1089-778X, 1941-0026
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