Hyper multi-objective evolutionary algorithm for multi-objective optimization problems

Multi-objective optimization problems (MOPs) are very common in practice. To solve MOPs, many kinds of multi-objective evolutionary algorithms (MOEAs) are proposed. However, different MOEAs have different performances for different MOPs. Therefore, it is a time-consuming task to choose a suitable MO...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Soft computing (Berlin, Germany) Jg. 21; H. 20; S. 5883 - 5891
Hauptverfasser:	Guo, Weian, Chen, Ming, Wang, Lei, Wu, Qidi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2017 Springer Nature B.V
Schlagworte:	Artificial Intelligence Computational Intelligence Control Decomposition Engineering Evolutionary algorithms Focus Genetic algorithms Mathematical Logic and Foundations Mechatronics Multiple objective analysis Objectives Optimization Robotics Group Multi-objective optimization problems Coverage rate Hyper multi-objective evolutionary algorithm
ISSN:	1432-7643, 1433-7479
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!