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Volume 18, Issue 1
Cable Laying Path Planning Based on Optimized Ant Colony Algorithm

Pingxian Dong, Fang Guo, Chen Chen, Xiaofan Song, Hui Wang, Pingping Bai, Huanruo Qi, Yiming Qian, Haojie Zhang & Yunhao Han

DOI: 10.4208/JICS-2023-005

J. Info. Comput. Sci. , 18 (2023), pp. 67-80.

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  • Abstract

To address the large error and low efficiency of traditional manual design in cable laying task, the computer-aided design optimized by Ant Colony Algorithm (ACA) is applied to cable laying path planning. The shortest path for cable laying is solved via the ACA's multi terminal path calculation for complex path planning. Furthermore, the planarized cable laying path is optimized via Gompertz function in aspects of pheromone restriction and self-adaptive adjustment of volatilization factor, thus improves the ACA in both convergence speed and global performance. The simulation results show that the optimized ant colony algorithm can quickly obtain the shortest cable laying path in the task of substation digital 3D cable laying, which saves the cost of manpower and materials, and improves the design accuracy.

  • Keywords

Ant colony algorithm (ACA), Cable laying, Pheromone, Volatilization factor, Convergence rate.

  • AMS Subject Headings

68Q25, 78M50

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{JICS-18-67, author = {Dong , PingxianGuo , FangChen , ChenSong , XiaofanWang , HuiBai , PingpingQi , HuanruoQian , YimingZhang , Haojie and Han , Yunhao}, title = {Cable Laying Path Planning Based on Optimized Ant Colony Algorithm}, journal = {Journal of Information and Computing Science}, year = {2024}, volume = {18}, number = {1}, pages = {67--80}, abstract = {

To address the large error and low efficiency of traditional manual design in cable laying task, the computer-aided design optimized by Ant Colony Algorithm (ACA) is applied to cable laying path planning. The shortest path for cable laying is solved via the ACA's multi terminal path calculation for complex path planning. Furthermore, the planarized cable laying path is optimized via Gompertz function in aspects of pheromone restriction and self-adaptive adjustment of volatilization factor, thus improves the ACA in both convergence speed and global performance. The simulation results show that the optimized ant colony algorithm can quickly obtain the shortest cable laying path in the task of substation digital 3D cable laying, which saves the cost of manpower and materials, and improves the design accuracy.

}, issn = {1746-7659}, doi = {https://doi.org/10.4208/JICS-2023-005}, url = {http://global-sci.org/intro/article_detail/jics/23721.html} }
TY - JOUR T1 - Cable Laying Path Planning Based on Optimized Ant Colony Algorithm AU - Dong , Pingxian AU - Guo , Fang AU - Chen , Chen AU - Song , Xiaofan AU - Wang , Hui AU - Bai , Pingping AU - Qi , Huanruo AU - Qian , Yiming AU - Zhang , Haojie AU - Han , Yunhao JO - Journal of Information and Computing Science VL - 1 SP - 67 EP - 80 PY - 2024 DA - 2024/12 SN - 18 DO - http://doi.org/10.4208/JICS-2023-005 UR - https://global-sci.org/intro/article_detail/jics/23721.html KW - Ant colony algorithm (ACA), Cable laying, Pheromone, Volatilization factor, Convergence rate. AB -

To address the large error and low efficiency of traditional manual design in cable laying task, the computer-aided design optimized by Ant Colony Algorithm (ACA) is applied to cable laying path planning. The shortest path for cable laying is solved via the ACA's multi terminal path calculation for complex path planning. Furthermore, the planarized cable laying path is optimized via Gompertz function in aspects of pheromone restriction and self-adaptive adjustment of volatilization factor, thus improves the ACA in both convergence speed and global performance. The simulation results show that the optimized ant colony algorithm can quickly obtain the shortest cable laying path in the task of substation digital 3D cable laying, which saves the cost of manpower and materials, and improves the design accuracy.

Dong , PingxianGuo , FangChen , ChenSong , XiaofanWang , HuiBai , PingpingQi , HuanruoQian , YimingZhang , Haojie and Han , Yunhao. (2024). Cable Laying Path Planning Based on Optimized Ant Colony Algorithm. Journal of Information and Computing Science. 18 (1). 67-80. doi:10.4208/JICS-2023-005
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