Journal of Systems Engineering and Electronics ›› 2012, Vol. 23 ›› Issue (6): 921-928.doi: 10.1109/JSEE.2012.00113

• SOFTWARE ALGORITHM AND SIMULATION • Previous Articles     Next Articles

Enhanced self-adaptive evolutionary algorithm for numerical optimization

Yu Xue1, Yi Zhuang1,*, Tianquan Ni2, Jian Ouyang1, and Zhou Wang3   

  1. 1. School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P. R. China;
    2. No.723 Institute of China Shipbuilding Industry Corporation, Yangzhou 225001, P. R. China;
    3. Science and Technology on Electron-optic Control Laboratory, Luoyang 471000, P. R. China
  • Online:2012-12-24 Published:2010-01-03

Abstract:

There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced self-adaptive evolutionary algorithm (ESEA) to overcome the demerits above. In the ESEA, four evolutionary operators are designed to enhance the evolutionary structure. Besides, the ESEA employs four effective search strategies under the framework of the self-adaptive learning. Four groups of the experiments are done to find out the most suitable parameter values for the ESEA. In order to verify the performance of the proposed algorithm, 26 state-of-the-art test functions are solved by the ESEA and its competitors. The experimental results demonstrate that the universality and robustness of the ESEA outperform its competitors.