An improved brain storm optimisation algorithm for energy-efficient train operation problem Online publication date: Wed, 28-Jul-2021
by Boyang Qu; Qian Zhou; Yongsheng Zhu; Jing Liang; Caitong Yue; Yuechao Jiao; Li Yan; Ponnuthurai Nagaratnam Suganthan
International Journal of Bio-Inspired Computation (IJBIC), Vol. 17, No. 4, 2021
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.
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