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Title: An improved differential evolution and its industrial application
Authors: Lai, JCY
Leung, FHF 
Ling, SH
Shi, EC
Keywords: Differential evolution
Evolutionary algorithm
Economic load dispatch
Issue Date: May-2012
Publisher: Scientific Research
Source: Journal of intelligent learning systems and applications, May 2012, v. 4, no. 2, p.81-97 How to cite?
Journal: Journal of intelligent learning systems and applications 
Abstract: In this paper, an improved Differential Evolution (DE) that incorporates double wavelet-based operations is proposed to solve the Economic Load Dispatch (ELD) problem. The double wavelet mutations are applied in order to enhance DE in exploring the solution space more effectively for better solution quality and stability. The first stage of wavelet operation is embedded in the DE mutation operation, in which the scaling factor is governed by a wavelet function. In the second stage, a wavelet-based mutation operation is embedded in the DE crossover operation. The trial population vectors are modified by the wavelet function. A suite of benchmark test functions is employed to evaluate the performance of the proposed DE in different problems. The result shows empirically that the proposed method out-performs signifycantly the conventional methods in terms of convergence speed, solution quality and solution stability. Then the proposed method is applied to the Economic Load Dispatch with Valve-Point Loading (ELD-VPL) problem, which is a process to share the power demand among the online generators in a power system for minimum fuel cost. Two different conditions of the ELD problem have been tested in this paper. It is observed that the proposed method gives satisfactory optimal costs when compared with the other techniques in the literature.
ISSN: 2150-8402 (print)
2150-8410 (online)
DOI: 10.4236/jilsa.2012.42008
Rights: Copyright © 2012 SciRes.
This is an open access article distributed under the Creative Commons License.
The article: Lai, J. C. Y., Leung, F. H. F., Ling, S. H., & Shi, E. C. (2012). An improved differential evolution and its industrial application. Journal of Intelligent Learning Systems and Applications, 4(2), 81-97 is available at
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