Int. J. of Wireless and Mobile Computing   »   2016 Vol.10, No.3

 

 

Title: Differential evolution algorithm with dynamic population scheme

 

Authors: Xinyu Zhou; Mingwen Wang; Jianyi Wan

 

Addresses:
School of Computer and Information Engineering, Jiangxi Normal University, Nanchang 330022, China
School of Computer and Information Engineering, Jiangxi Normal University, Nanchang 330022, China
School of Computer and Information Engineering, Jiangxi Normal University, Nanchang 330022, China

 

Abstract: Differential evolution (DE) is an efficient population-based evolutionary algorithm for solving optimisation problems. Its control parameters have significant influence on performance. In the past few years, how to adjust the scaling factor F and crossover probability CR has attracted a lot of attention. However, few works have been focused on the parameter of population size. So in this paper, we designed a dynamic population scheme to manage the population size of DE. This scheme mainly consists of two parts: the logistic model and opposition-based learning. The first part is dedicated to calculate how many new individuals should be added into the population, while the second part is used to generate these new individuals. A series of experiments is conducted on 25 well-known benchmark functions including shifted and rotated ones. Results show that our approach shows promising performance.

 

Keywords: differential evolution; population size; logistic modelling; opposition-based learning; OBL; dynamic population.

 

DOI: 10.1504/IJWMC.2016.077212

 

Int. J. of Wireless and Mobile Computing, 2016 Vol.10, No.3, pp.261 - 271

 

Available online: 23 Jun 2016

 

 

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