Multi-objective Optimization Algorithm of Train Operation Process Based on Incorporated Preference Information

Journal of Transportation Systems Engineering and Information Technology ›› 2017, Vol. 17 ›› Issue (6): 133-140.

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Multi-objective Optimization Algorithm of Train Operation Process Based on Incorporated Preference Information

WANG Long-da ¹,WANG Xing-cheng ¹, ZOU Gang-wen², SUN Da-wei ¹, LIU Gang ³

1. Information Science and Technology College, Dalian Maritime University, Dalian 116023, Liaoning, China; 2. CRRC Qiqihar Rolling Stock CO., Ltd., Dalian 116023, Liaoning, China; 3. College of Mechanical Engineering, Inner Mongolia University for Nationalities, Tongliao 028005, Inner Mongolia, China

Received:2017-04-27 Revised:2017-08-14 Online:2017-12-25 Published:2017-12-25

融入偏好信息的列车运行过程多目标优化算法

王龙达¹，王兴成*¹，邹广闻²，孙大伟¹，刘罡³

1. 大连海事大学信息技术学院，辽宁大连116023；2. 中车齐齐哈尔车辆有限公司大连研发中心，辽宁大连116023； 3. 内蒙古民族大学机械工程学院，内蒙古通辽028005

作者简介:王龙达(1986-)，男，辽宁大连人，博士生.
基金资助:
国家自然科学基金/ National Natural Science Foundation of China (60574018)；内蒙古自治区高等学校科学研究基金/ Scientific Research Projects Foundation of the Inner Mongolia Autonomous Region Higher Education Institutions (NJZZ16178)；内蒙古自治区自然科学基金/ The Inner Mongolia Autonomous Region Natural Science Foundation (2017BS0605)；内蒙古自治区高等学校青年科技英才支持计划基金/Program Foundation for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region(NJYT-17-B34).

Abstract

Abstract:

The optimization of train operation process is a sophisticated problem which is multiple objectives, large delay and nonlinearity. In order to solve the problem, the multi- objective optimization model of train operation process is established, which regards train kinetic equation as constraint, and energy consumption, comfort, precise parking and punctuality as control targets, the optimization of train operation process based on multi-objective genetic particle swarm optimization using incorporated preference information is proposed. The advantage of the proposed method is summarized as follows: the proposed strategy with incorporated preference information can better maintain the diversity of the population by controlling the distribution of individuals of the population in the solution space. Thus, there is a more obvious role of global convergence in the evolution process. The proposed algorithm has better performance and better search results in the same situation of train and line.

Key words: railway transportation, multi-objective optimization of train operation, preference information, genetic particle swarm optimization, train operation process

摘要：

列车运行过程优化是一个多目标、大滞后、非线性的极其复杂的优化问题.为了更好地解决上述问题，以列车能耗、舒适性、停靠准确性和运行时间为控制目标，以列车运动动力学方程为约束，建立了列车运行过程的多目标优化模型，提出了一种融入偏好信息的列车运行过程多目标遗传粒子群算法.提出的改进策略具有以下优点，在融入偏好信息的基础上通过控制粒子群中个体在解空间的分布能够更好地保持粒子群多样性，从而在进化过程中具有更明显的全局收敛的指向作用.仿真得到的速度距离曲线表明，在列车及其运行线路相同的情况下，本文所提出的算法性能较佳、寻优结果较好.

关键词: 铁路运输, 列车多目标优化, 偏好信息, 遗传粒子群算法, 列车运行过程

CLC Number:

U268.7

WANG Long-da,WANG Xing-cheng, ZOU Gang-wen, SUN Da-wei, LIU Gang. Multi-objective Optimization Algorithm of Train Operation Process Based on Incorporated Preference Information[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(6): 133-140.

王龙达，王兴成，邹广闻，孙大伟，刘罡. 融入偏好信息的列车运行过程多目标优化算法[J]. 交通运输系统工程与信息, 2017, 17(6): 133-140.

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Multi-objective Optimization Algorithm of Train Operation Process Based on Incorporated Preference Information

融入偏好信息的列车运行过程多目标优化算法

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