Application of Discrete Differential Evolution Algorithm in the optimized Problem of Motor Train-sets Scheduling

• Published in: IEEE International Conference on Control and Automation (Print) pp. 1585 - 1590
• Main Authors: Li, Jiajun, Zhao, Wenjing, Zhang, Chunmei, Zhang, Bo
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2019
• Subjects: adaptive adjustment; Decoding; Directed graphs; Dispatching; Encoding; motor train-sets scheduling; Motors; Optimization; priority coding; Scheduling; Shortest path problem; Simulation; Stochastic processes; stochastic replacement algorithm
• ISSN: 1948-3457
• DOI: 10.1109/ICCA.2019.8899644

In order to improve the utilization efficiency of motor train-sets, a Randomized Swap Differential Evolution algorithm (RSDE) is proposed. For the motor train-sets scheduling model with multiple hub stations, the "adjacent node" is taken as the selection range of the connected trains, and the optimization model of the motor train-sets based on the directed graph is established. To satisfy the continuity, an improved priority decoding method, applicable to any situation where trains run in pair, is proposed for generating the initial population and for decoding the individual into the operational tasks that motor train-sets undertakes in turn. In order to apply the differential evolution algorithm to the combinatorial optimization problem, a random permutation operator is proposed based on group theory. Considering the difference of evolutionary information carried by individuals, the adaptive regulatory shrinkage factor is proposed. A novel selection strategy is adopted to keep trial subjects as much as possible. The Wuhan-Guangzhou passenger line is taken as the research object, and the performance of the proposed algorithm is tested. The simulation results show that the algorithm can achieve better optimization performance compared with other several algorithms listed.