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2015 | OriginalPaper | Buchkapitel

A Parallel Version of Differential Evolution Based on Resilient Distributed Datasets Model

verfasst von : Changshou Deng, Xujie Tan, Xiaogang Dong, Yucheng Tan

Erschienen in: Bio-Inspired Computing -- Theories and Applications

Verlag: Springer Berlin Heidelberg

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Abstract

MapReduce is a popular cloud computing platform which has been widely applied in large-scale data-intensive fields. However, when dealing with computation extensive tasks, particularly, iterative computation, frequent loading Map and Reduce processes will lead to overhead. Resilient distributed datasets model which has been implemented in Spark, is an in-memory clustering computing which can overcome this shortcoming efficiently. In this paper, we attempt to use resilient distributed datasets model to parallelize Differential Evolution algorithm. A wide range of benchmark problems have been adopted to conduct numerical experiment, and the speedup of PDE due to use of resilient distributed datasets model is demonstrated. The results show us that resilient distributed datasets model is a potential way to parallelize evolutionary algorithm.

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Titel: A Parallel Version of Differential Evolution Based on Resilient Distributed Datasets Model
verfasst von: Changshou Deng
Xujie Tan
Xiaogang Dong
Yucheng Tan
Verlag: Springer Berlin Heidelberg
Buch: Bio-Inspired Computing -- Theories and Applications
Print ISBN: 978-3-662-49013-6

Electronic ISBN: 978-3-662-49014-3

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-662-49014-3_8

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