Title: SparkCUDE: a spark-based differential evolution for large-scale global optimisation

Authors: Hu Peng; Xujie Tan; Changshou Deng; Shunxu Peng

Addresses: School of Information Science and Technology, Jiujiang University, Jiujiang, China ' School of Information Science and Technology, Jiujiang University, Jiujiang, China ' School of Information Science and Technology, Jiujiang University, Jiujiang, China ' Library, Jiujiang University, Jiujiang, China

Abstract: Differential evolution (DE) is one of the efficient evolutionary algorithms over larger-scale global optimisation problems. Recently, the new cloud computing models (such as Spark) have drawn attentions to deal with larger-scale global optimisation problems. Spark provides effective support for iterative algorithms. However, we have noted that simultaneous combination of the excellent DE variant and the improved spark computing model to enhance the optimisation performance and reduce the computation times has not exploited. In this paper, we propose a Spark-based DE algorithm for larger-scale global optimisation problems, called SparkCUDE, in which the Spark computation model with ring topology is introduced and the CUDE algorithm is employed as the internal optimiser. The original CUDE was proposed in our previous work, in which uniform local search enhances exploitation ability and the commensal learning is proposed to adaptively select optimal mutation strategy and parameter setting simultaneously under the same criteria. Experimental studies are conducted on the benchmark functions of CEC2010 on large-scale global optimisation. Comprehensive experiments demonstrate the effectiveness and efficiency of the proposed approach.

Keywords: differential evolution; DE; large-scale global optimisation; spark; migration strategy.

DOI: 10.1504/IJHPSA.2017.092390

International Journal of High Performance Systems Architecture, 2017 Vol.7 No.4, pp.211 - 222

Received: 06 Jun 2017
Accepted: 16 Oct 2017
Published online: 17 Jun 2018 *

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