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The Scientific World Journal
Volume 2013 (2013), Article ID 969734, 12 pages
Research Article

An Adaptive Cauchy Differential Evolution Algorithm for Global Numerical Optimization

1Department of Computer Engineering, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Suwon 440-746, Republic of Korea
2Robot Research Division, Daegu Gyeongbuk Institute of Science and Technology (DGIST), 50-1 Sang-ri, Hyeonpung-meyeon, Daegu 711-873, Republic of Korea

Received 3 May 2013; Accepted 6 June 2013

Academic Editors: P. Agarwal, S. Balochian, V. Bhatnagar, J. Yan, and Y. Zhang

Copyright © 2013 Tae Jong Choi 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.


Adaptation of control parameters, such as scaling factor (), crossover rate (CR), and population size (NP), appropriately is one of the major problems of Differential Evolution (DE) literature. Well-designed adaptive or self-adaptive parameter control method can highly improve the performance of DE. Although there are many suggestions for adapting the control parameters, it is still a challenging task to properly adapt the control parameters for problem. In this paper, we present an adaptive parameter control DE algorithm. In the proposed algorithm, each individual has its own control parameters. The control parameters of each individual are adapted based on the average parameter value of successfully evolved individuals’ parameter values by using the Cauchy distribution. Through this, the control parameters of each individual are assigned either near the average parameter value or far from that of the average parameter value which might be better parameter value for next generation. The experimental results show that the proposed algorithm is more robust than the standard DE algorithm and several state-of-the-art adaptive DE algorithms in solving various unimodal and multimodal problems.