Int. J. of Wireless and Mobile Computing   »   2016 Vol.11, No.3

 

 

Title: Improving the JADE algorithm by clustering successful parameters

 

Authors: Zhijian Li; Jinglei Guo; Shengxiang Yang

 

Addresses:
School of Computer Science, Central China Normal University, Wuhan 430079, China
School of Computer Science, Central China Normal University, Wuhan 430079, China
Centre for Computational Intelligence (CCI), School of Computer Science and Informatics, De Montfort Univesity, Leicester LE1 9BH, UK

 

Abstract: Differential evolution (DE) is one of the most powerful and popular evolutionary algorithms for real parameter global optimisation problems. However, the performance of DE highly depends on the selection of control parameters, e.g. the population size, scaling factor and crossover rate. How to set these parameters is a challenging task because they are problem dependent. In order to tackle this problem, a JADE variant, denoted CJADE, is proposed in this paper. In the proposed algorithm, the successful parameters are clustered with the k-means clustering algorithm to reduce the impact of poor parameters. Simulation results show that CJADE is better than, or at least comparable to, several state-of-the-art DE algorithms.

 

Keywords: differential evolution; k-means clustering; successful parameters; JADE algorithm; simulation.

 

DOI: 10.1504/IJWMC.2016.10002147

 

Int. J. of Wireless and Mobile Computing, 2016 Vol.11, No.3, pp.190 - 197

 

Submission date: 25 Jun 2016
Date of acceptance: 01 Sep 2016
Available online: 24 Dec 2016

 

 

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