A Differential Evolution with Multi-factor Ranking Based Parameter Adaptation for Global Optimization
Jing Wei, Zuling Wang, Yangyan Xu, Ze Chen · 2021
The performance of differential evolution (DE) algorithm depends critically on the setting of mutation factor F and crossover rate CR. In this paper, a multi-factor ranking based parameter adaptation scheme is proposed to properly set the value of F and CR. The proposed adaptation scheme includes a parameter storage and distribution mechanism. The parameter storage mechanism is used to produce a suitable value by considering the multi-factor ranking scheme, which can help the algorithm enhance its exploration ability in the early stage. The parameter distribution mechanism is a layered parameter calculation strategy, in which the individuals generate new parameters based on multi-factor ranking properties. This mechanism is helpful to improve the exploitation capability of DE. The performance of the proposed method has been evaluated on CEC2014 and CEC2015 test suites and compared with related methods. The results show our method could achieve good performance and outperform related methods.