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The Scientific World Journal
Volume 2013, Article ID 172193, 8 pages
http://dx.doi.org/10.1155/2013/172193
Research Article

Complexity Reduction in the Use of Evolutionary Algorithms to Function Optimization: A Variable Reduction Strategy

1Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, 47 Yanzheng Street, Changsha, Hunan 410073, China
2Department of Electrical & Computer Engineering, University of Alberta, Edmonton, AB, Canada T6R 2V4
3Warsaw School of Information Technology, Newelska 6, Warsaw, Poland
4School of Civil Engineering and Architecture, Central South University, Changsha, Hunan 410004, China
5School of Geosciences and Info-Physics, Central South University, Changsha, Hunan, China

Received 13 August 2013; Accepted 8 September 2013

Academic Editors: P. Bala and P. K. Egbert

Copyright © 2013 Guohua Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Discovering and utilizing problem domain knowledge is a promising direction towards improving the efficiency of evolutionary algorithms (EAs) when solving optimization problems. We propose a knowledge-based variable reduction strategy (VRS) that can be integrated into EAs to solve unconstrained and first-order derivative optimization functions more efficiently. VRS originates from the knowledge that, in an unconstrained and first-order derivative optimization function, the optimal solution locates in a local extreme point at which the partial derivative over each variable equals zero. Through this collective of partial derivative equations, some quantitative relations among different variables can be obtained. These variable relations have to be satisfied in the optimal solution. With the use of such relations, VRS could reduce the number of variables and shrink the solution space when using EAs to deal with the optimization function, thus improving the optimizing speed and quality. When we apply VRS to optimization problems, we just need to modify the calculation approach of the objective function. Therefore, practically, it can be integrated with any EA. In this study, VRS is combined with particle swarm optimization variants and tested on several benchmark optimization functions and a real-world optimization problem. Computational results and comparative study demonstrate the effectiveness of VRS.