Title: Generalised opposition-based differential evolution for frequency modulation parameter optimisation

Authors: Hui Wang; Wenjun Wang; Huasheng Zhu; Hui Sun

Addresses: School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China ' School of Business Administration, Nanchang Institute of Technology, Nanchang 330099, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China

Abstract: This paper presents an improved differential evolution (DE) algorithm to solve frequency modulation (FM) parameter optimisation problems. The proposed approach is called generalised opposition-based differential evolution (GODE), which employs generalised opposition-based learning (GOBL) to accelerate the convergence rate of original DE. To solve the FM problem, three different kinds of parameter optimisation models are verified in the experiments. Simulation results show that our approach achieves better matching than three other similar algorithms.

Keywords: differential evolution; generalised opposition-based learning; GOBL; frequency modulation; parameter optimisation; global optimisation; modelling; simulation.

DOI: 10.1504/IJMIC.2013.053543

International Journal of Modelling, Identification and Control, 2013 Vol.18 No.4, pp.372 - 379

Published online: 29 Apr 2013 *

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