Train Operation Strategy Optimization Based on Velocity Mutation Particle Swarm Algorithm

SHA Quan; CANG Xiao-di

Chinese

您当前的位置：

首页 >

文章列表页 >

Train Operation Strategy Optimization Based on Velocity Mutation Particle Swarm Algorithm

更新时间：2026-09-04

- Train Operation Strategy Optimization Based on Velocity Mutation Particle Swarm Algorithm
- Vol. 31, Issue 3, Pages: 130-133(2014)
- 作者机构：
  
  上海应用技术学院轨道交通学院,上海,201418
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2014
- 稿件说明：
移动端阅览
沙泉, 仓小娣. 基于速度变异粒子群算法的列车运行策略优化[J]. 微电子学与计算机, 2014, 31(3): 130-133.

SHA Quan, CANG Xiao-di. Train Operation Strategy Optimization Based on Velocity Mutation Particle Swarm Algorithm[J]. 2014, 31(3): 130-133.
沙泉, 仓小娣. 基于速度变异粒子群算法的列车运行策略优化[J]. 微电子学与计算机, 2014, 31(3): 130-133. DOI：

SHA Quan, CANG Xiao-di. Train Operation Strategy Optimization Based on Velocity Mutation Particle Swarm Algorithm[J]. 2014, 31(3): 130-133. DOI：

摘要

为了提高列车在运行过程中的能源利用率，采用速度变异的粒子群算法，对列车运行控制策略进行优化.根据列车运行的机械能理论和能耗理论，建立了以能耗为目标，满足距离、时间和速度约束的列车运行仿真模型，在确定的工况序列条件下，求解列车最优控制策略.改进粒子群算法按照一定的概率，对最小速度进行变异，拓展了粒子有效搜索空间，从而改善优化结果.算例仿真验证了该方法的有效性和合理性.

Abstract

For the purpose of improving train running efficiency

a velocity mutation particle swarm optimization (VMPSO) algorithm is applied in optimization of train control strategy.A train operation and movement model is presented based on mechanical theory of train control process and train energy consumption theory.The distance

interval and speed constraints are taken into account in this model.Under certain input control sequences

this optimization problem is solved by VMPSO algorithm.The minimal velocity is mutated according to some probability in PSO algorithm to exploit the searching scope of optimal solution.Numerical simulation is presented to verify the effectiveness and reasonableness of the method.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on the design of blockchain-based AI-generated content traceability framework

Research on assembly action recognition based on MSS-YOLO-MPHands fusion architecture

Steganography model guided by attention mechanism and improved dense convolution

Lightweight countermeasures against Spectre attacks on the branch prediction unit

Simulation on the DC characteristics of double heterojunction p-GaN HEMT with AlN/GaN hybrid buffer layer

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰