Title: An improved brain storm optimisation algorithm for energy-efficient train operation problem

Authors: Boyang Qu; Qian Zhou; Yongsheng Zhu; Jing Liang; Caitong Yue; Yuechao Jiao; Li Yan; Ponnuthurai Nagaratnam Suganthan

Addresses: School of Electrical and Information Engineering, Zhongyuan University of Technology, Zhengzhou, Henan, China ' School of Electrical and Information Engineering, Zhongyuan University of Technology, Zhengzhou, Henan, China ' School of Electrical and Information Engineering, Zhongyuan University of Technology, Zhengzhou, Henan, China ' School of Electrical Engineering, Zhengzhou University, Zhengzhou, Henan, China ' School of Electrical Engineering, Zhengzhou University, Zhengzhou, Henan, China ' School of Electrical and Information Engineering, Zhongyuan University of Technology, Zhengzhou, Henan, China ' School of Electrical and Information Engineering, Zhongyuan University of Technology, Zhengzhou, Henan, China ' School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Avenue, Singapore

Abstract: This paper presents a new method to determine the optimal driving strategies of the train using an improved brain storm optimisation (IBSO) algorithm. In the proposed method, the idea of successful-parent-selecting frame is applied to improve the original brain storm optimisation (BSO) algorithm avoiding premature convergence in evolutionary process while dealing with complex problems. The objective of the algorithm is to minimise energy consumption of the train by finding the switching points. Furthermore, the speed limits, gradients, maximum acceleration and deceleration as well as the maximum traction and braking force varying with speed are taken into consideration to meet practical constraints. Finally the comparison simulations among four algorithms show that the energy-efficient train operation strategy obtained by IBSO algorithm are more superior under the same conditions.

Keywords: evolutionary algorithms; brain storm optimisation; BSO; energy-efficient train operation.

DOI: 10.1504/IJBIC.2021.116549

International Journal of Bio-Inspired Computation, 2021 Vol.17 No.4, pp.236 - 245

Accepted: 29 Jan 2020
Published online: 28 Jul 2021 *

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