.png){kind=icon}
.png){kind=icon}
Return
Complex parameter estimation based on adaptive population renewal-based differential evolution algorithm and its application
.png){kind=icon}
PRE
AI
DOI：10.1016/j.engappai.2026.113916.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Complex parameter estimation is widely used in system control, its accuracy plays a crucial role in system performance improvement. Therefore, to address the complex parameter estimation problem, this paper proposes an adaptive population renewal-based differential evolution (APRDE) algorithm. Inspired by the theory of natural selection, a population renewal strategy is designed before each iteration to steer the population towards the quest for the global optimum. Meanwhile, adaptive scaling factor and adaptive crossover factor are further proposed respectively, increasing the population diversity and enhancing the algorithm's global search capability. When compared to other algorithms evaluated on benchmark functions, the APRDE algorithm excels in terms of both convergence accuracy and speed. In recent years, research on proton exchange membrane fuel cell (PEMFC) systems has gained significant attention. Based on this, the APRDE algorithm is used to estimate parameters for the PEMFC model. Experimental results reveal that, in contrast to existing algorithms, the proposed approach offers greater dynamism and efficiency in PEMFC model parameter estimation.
Journal
IF:
8
Papers:
5.3K
Citations:
3.5W
View more organizations