.png){kind=icon}
Return
Dynamic hybrid mechanism-based differential evolution algorithm and its application
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2022.118834.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In order to effectively schedule railway train delay, an adaptive cooperative co-evolutionary differential evo-lution with dynamic hybrid mechanism of the quantum evolutionary algorithm and genetic algorithm, named QGDECC is designed in this paper. In the QGDECC, the quantum variable decomposition strategy is designed by utilizing qubit string to decompose variables adaptively according to the coevolution performance. Then the increment mutation method is proposed to improve the convergence speed which make full use of searched evolution information. Besides, the parameter adaptive strategy is deeply explored for strengthening the robust of the algorithm. The QGDECC with global search capability is employed to realize a railway train delay scheduling method for effectively eliminating the impact of train delay. Finally, several benchmark functions and actual train operation data are selected to verify the optimization performance of QGDECC. The experimental results show that QGDECC has higher adaptability, faster convergence speed and accuracy. The train delay scheduling method can effectively eliminate the impact of delay on the railway network, and minimize the gap between the rescheduled train schedule and the original train schedule.
Keywords:
Differential evolution
Hybrid mechanism
Variable decomposition
Parameter adaptation
Increment mutation
Cooperative co-evolution
Train scheduling
Journal
IF:
7.5
Papers:
3.0W
Citations:
10.2W
Organization
Cited Papers
ShareSaveShareSave
An improved differential evolution algorithm and its application in optimization problem.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
ShareSaveShareSave
View more