Late parallelization and feedback approaches for distributed computation of evolutionary multi-objective optimization algorithms

Distributing of the multi-objective evolutionary algorithms into various computational devices in a parallel fashion is a method for speeding up the execution time of the algorithms. When the processors are increased in number, the gain from parallelization decreases. For this reason, the aim of the...

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Veröffentlicht in:	Neural computing & applications Jg. 30; H. 3; S. 723 - 733
Hauptverfasser:	Altinoz, O. Tolga, Deb, Kalyanmoy
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.08.2018 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Computation Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Double cusps Evolutionary algorithms Feedback Image Processing and Computer Vision Iscmi15 Multiple objective analysis Parallel processing Population (statistical) Probability and Statistics in Computer Science Processors Reference point-based NSGA-II Parallelization Distributing computing
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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