Volume 8, Issue 3, June 2015, Pages 561 - 590
Optimal Tunnel Coverage Problem by One New Nature-Inspired Energy Conservation Optimization
Jun Liu, Tianyun Shi, Ping Li, Xuemei Ren, Hongbin Ma
Received 25 September 2014, Accepted 25 January 2015, Available Online 1 June 2015.
- https://doi.org/10.1080/18756891.2015.1036223How to use a DOI?
- Energy conversation optimization, optimal tunnel coverage problem, sensor network, railway transportation
- This paper mainly provides one new nature inspired Energy Conversation Optimization (ECO) method to search for the optimal sensor position of sensor network in the typical tunnels, fully covering the tunnel and dynamically locating the high-speed train in the tunnel. Firstly, one objective of this study is to briefly introduce the framework of transmitting the sensor's data in the railway network, the objectives and the constraints of the optimal coverage problem. Secondly, another objective of this paper is to provide some fundamental assumptions and concepts of ECO algorithm, together with the corresponding convergence analysis and the main steps of ECO algorithm. Additionally, the ECO algorithm is mainly utilized to address the optimal tunnel coverage problem. Numerical results mainly concentrate on the effectiveness of ECO algorithm comparing with the PSO algorithm and deal with four typical optimal coverage problems in the presence of the line tunnel, the indirect line tunnel and the complicated tunnels, etc. Finally, the ECO algorithm can offer the guideline for the smallest number of sensors and the corresponding positions in the given tunnel, fully covering the whole tunnel and providing the location of high-speed trains.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Jun Liu AU - Tianyun Shi AU - Ping Li AU - Xuemei Ren AU - Hongbin Ma PY - 2015 DA - 2015/06 TI - Optimal Tunnel Coverage Problem by One New Nature-Inspired Energy Conservation Optimization JO - International Journal of Computational Intelligence Systems SP - 561 EP - 590 VL - 8 IS - 3 SN - 1875-6883 UR - https://doi.org/10.1080/18756891.2015.1036223 DO - https://doi.org/10.1080/18756891.2015.1036223 ID - Liu2015 ER -