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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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 20; H. 5; S. 773 - 791
Hauptverfasser:	Ran Cheng, Yaochu Jin, Olhofer, Markus, Sendhoff, Bernhard
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 01.10.2016
Schlagworte:	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
Online-Zugang:	Volltext
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