FD-DE: Differential Evolution with fitness deviation based adaptation in parameter control

Zhenyu Meng; Zhenghao Song; Xueying Shao; Junyuan Zhang; Huarong Xu

doi:10.1016/j.isatra.2023.05.005

FD-DE: Differential Evolution with fitness deviation based adaptation in parameter control

ISA Trans. 2023 Aug:139:272-290. doi: 10.1016/j.isatra.2023.05.005. Epub 2023 May 13.

Authors

Zhenyu Meng¹, Zhenghao Song², Xueying Shao², Junyuan Zhang², Huarong Xu³

Affiliations

¹ Institute of Artificial Intelligence, Fujian University of Technology, Fuzhou, China; Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China. Electronic address: mzy1314@gmail.com.
² Institute of Artificial Intelligence, Fujian University of Technology, Fuzhou, China.
³ Department of Computer Science and Technology, Xiamen University of Technology, Xiamen, China.

PMID: 37230905
DOI: 10.1016/j.isatra.2023.05.005

Abstract

Differential Evolution (DE) is arguably one of the most powerful stochastic optimization algorithms for different optimization applications, however, even the state-of-the-art DE variants still have many weaknesses. In this study, a new powerful DE variant for single-objective numerical optimization is proposed, and there are several contributions within it: First, an enhanced wavelet basis function is proposed to generate scale factor F of each individual in the first stage of the evolution; Second, a hybrid trial vector generation strategy with perturbation and t-distribution is advanced to generate different trial vectors regarding different stages of the evolution; Third, a fitness deviation based parameter control is proposed for the adaptation of control parameters; Fourth, a novel diversity indicator is proposed and a restart scheme can be launched if necessary when the quality of the individuals is detected bad. The novel algorithm is validated using a large test suite containing 130 benchmarks from the universal test suites on single-objective numerical optimization, and the results approve the big improvement in comparison with several well-known state-of-the-art DE variants. Moreover, our algorithm is also validated under real-world optimization applications, and the results also support its superiority.

Keywords: Differential evolution; Fitness deviation; Parameter control; Population stagnation.