research-article

Optimization of Train Control Strategy for Energy Saving and Time Precision Using Multi-Objective Cuckoo Search Algorithm

Authors:

Xin Liu,

Shenghua DaiAuthors Info & Claims

CSAE '18: Proceedings of the 2nd International Conference on Computer Science and Application Engineering

Article No.: 183, Pages 1 - 5

https://doi.org/10.1145/3207677.3278087

Published: 22 October 2018 Publication History

Get Access

Abstract

In1 this paper, multi-objective optimization problem of train operation is discussed. The optimization model of train operation is established subject to security, stop position, passenger comfort level with energy consumption and running time regarded as the optimization indexes. Incorporated with leader selection strategy, basic cuckoo search algorithm is expanded to solve optimization problem with multiple objectives. On the basis of line information and management condition, the multiple objective cuckoo search algorithm is applied to calculate the train optimization model, and a set of balanced running control strategies is obtained after calculation. The simulation result shows that by applying the balanced strategy to train operation optimization problem, energy consumption is reduced by 10.1%-30.4% while guaranteeing running time is less than scheduled time, stop position and comfort level also achieve satisfied result. Then it's verified that improved algorithm can handle running situation with speed limit in section.

References

[1]

Mao BH, and He TJ. 2000. A general-purposed simulation system on train movement. Journal of The China Railway Society, 22, 1--6.

Google Scholar

[2]

He HY, and Zhu JL. 2000. Design of Software for Traction Calculation and Operation Schematic Diagram of Trains. Journal of Southwest Jiaotong University, 22, 1--6.

Google Scholar

[3]

YU J, Zheng HY, and Qian QQ. 2010. Study on Multi-objective Train Control Based on Hybrid Particle Swarm Optimization. Journal of The China Railway Society, 32, 38--42.

Google Scholar

[4]

Tang T, and Xun J. 2016. Research on energy-efficient driving strategy in Beijing Yizhuang line. Journal of Beijing Jiaotong University, 40, 19--24.

Google Scholar

[5]

Lin C, Fang XQ, Zhao X, Zhang Q, and Liu X. 2017. Study on energy-saving optimization of train coasting control based on multi-population Genetic Algorithm. 2017 3rd International Conference on Control, Automation and Robotics(ICCAR), Nagoya, 627--632.

Google Scholar

[6]

Lu S, Yang J, Xue F, Ting TO, and Zhu H. 2017. Partial speed trajectory optimization for urban rail vehicles with considerations on motor efficiency. 2017 IEEE 20th International Conference on Intelligent Transportation Systems(ITSC), Yokohama, 1--6.

Google Scholar

[7]

YU J, Zheng HY, and Qian QQ. 2009. Study on Multi-objective Particle Swarm Optimization Algorithm Based on Preference. Control and Decision, 24, 66--70.

Google Scholar

[8]

Yang X S, and Deb S. 2010. Cuckoo Search via Levy Flights. Mathematics. 210--214.

Google Scholar

[9]

FU Q, GE HW, and SU SZ. 2016. Particle swarm optimization algorithm with firefly behavior and Levy flight. Journal of Computer Applications, 36, 3298--3302.

Google Scholar

[10]

Zitzler E, and Thiele L. 1999. Multi-objective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 3, 257--271.

Digital Library

Google Scholar

Cited By

View all

Makhadmeh SAwadallah MKassaymeh SAl-Betar MSanjalawe YKouka SAl-Redhaei A(2025)Recent advances in Multi-objective Cuckoo Search Algorithm, its variants and applicationsArchives of Computational Methods in Engineering10.1007/s11831-025-10240-9Online publication date: 9-Feb-2025
https://doi.org/10.1007/s11831-025-10240-9
He JQiao DZhang C(2023)On-time and energy-saving train operation strategy based on improved AGA multi-objective optimizationProceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit10.1177/09544097231203271238:5(511-519)Online publication date: 21-Sep-2023
https://doi.org/10.1177/09544097231203271
Sharma SRizvi SSharma V(2019)A Framework for Optimization of Software Test Cases Generation using Cuckoo Search Algorithm2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence)10.1109/CONFLUENCE.2019.8776898(282-286)Online publication date: Jan-2019
https://doi.org/10.1109/CONFLUENCE.2019.8776898

Index Terms

Optimization of Train Control Strategy for Energy Saving and Time Precision Using Multi-Objective Cuckoo Search Algorithm
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies

Recommendations

Multi-objective optimization using BFO algorithm
ICIC'11: Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications

This paper describes a novel bacterial foraging optimization (BFO) approach to multi-objective optimization, called Multi-objective Bacterial Foraging Optimization (MBFO). The search for Pareto optimal set of multi-objective optimization problems is ...
On-line Optimization of Energy-saving Train Control using Bacteria Foraging Algorithm
ICACS '18: Proceedings of the 2nd International Conference on Algorithms, Computing and Systems

Reducing the energy consumption has been a significant issue in urban transit operations. Considering the prior constraint from the operational schedule, the online optimization of the speed curve between successive stations is capable of improving the ...
MOGOA algorithm for constrained and unconstrained multi-objective optimization problems

Grasshopper Optimization Algorithm (GOA) was modified in this paper, to optimize multi-objective problems, and the modified version is called Multi-Objective Grasshopper Optimization Algorithm (MOGOA). An external archive is integrated with the GOA for ...

Comments

Information & Contributors

Information

Published In

CSAE '18: Proceedings of the 2nd International Conference on Computer Science and Application Engineering

October 2018

1083 pages

ISBN:9781450365123

DOI:10.1145/3207677

Conference Chair:
Ali Emrouznejad,
Program Chair:
Zhihong Qian

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 October 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

CSAE '18

CSAE '18: The 2nd International Conference on Computer Science and Application Engineering

October 22 - 24, 2018

Hohhot, China

Acceptance Rates

CSAE '18 Paper Acceptance Rate 189 of 383 submissions, 49%;

Overall Acceptance Rate 368 of 770 submissions, 48%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
61
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Makhadmeh SAwadallah MKassaymeh SAl-Betar MSanjalawe YKouka SAl-Redhaei A(2025)Recent advances in Multi-objective Cuckoo Search Algorithm, its variants and applicationsArchives of Computational Methods in Engineering10.1007/s11831-025-10240-9Online publication date: 9-Feb-2025
https://doi.org/10.1007/s11831-025-10240-9
He JQiao DZhang C(2023)On-time and energy-saving train operation strategy based on improved AGA multi-objective optimizationProceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit10.1177/09544097231203271238:5(511-519)Online publication date: 21-Sep-2023
https://doi.org/10.1177/09544097231203271
Sharma SRizvi SSharma V(2019)A Framework for Optimization of Software Test Cases Generation using Cuckoo Search Algorithm2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence)10.1109/CONFLUENCE.2019.8776898(282-286)Online publication date: Jan-2019
https://doi.org/10.1109/CONFLUENCE.2019.8776898

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Multi-objective optimization using BFO algorithm

On-line Optimization of Energy-saving Train Control using Bacteria Foraging Algorithm

MOGOA algorithm for constrained and unconstrained multi-objective optimization problems

Comments

Information

Published In

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations