A parameter adaptive DE algorithm on real-parameter optimization

Issue title: Special section: Intelligent data analysis and applications & smart vehicular technology, communications and applications

Guest editors: Valentina Emilia Balas and Lakhmi C. Jain

Article type: Research Article

Authors: Pan, Jeng-Shyang^{a; d} | Yang, Cheng^{b; d} | Meng, Fanjia^c | Chen, Yuxin^{b; d} | Meng, Zhenyu^{b; d; *}

Affiliations: [a] College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China | [b] Institute of Artificial Intelligence, Fujian University of Technology, Fuzhou, China | [c] Guanzhuang Central Primary School of Zhangqiu District, Jinan, China | [d] Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China

Correspondence: [*] Corresponding author. Zhenyu Meng. E-mail: [email protected].

Abstract:  Differential Evolution (DE) algorithm generates a population of individuals by encoding with a floating point vector, and it is a simple and effective population-based stochastic optimization algorithm for global optimization of continuous space. Because of its excellent performance, DE variants can be applied in a wide range of applications in science and engineering. However, the performance of DE is sensitive to the choice of trial vector generation strategy and the associated control parameters. Therefore, it is necessary to choose appropriate mutation strategy and control parameters when tackling optimization applications. In this paper, an adaptive update mechanism is proposed to update control parameters F and Cr. The experimental results are verified on the CEC 2013 test suite which contains 28 benchmark functions for the evaluation of single objective real parameter optimization. The proposed algorithm is compared with jDE, iwPSO and ccPSO, and experiment results show its good performance.

Keywords: Adaptive update mechanism, differential evolution, real parameter optimization, stochastic optimization

DOI: 10.3233/JIFS-179665

Journal: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5775-5786, 2020