Research on Ship Collision Avoidance Path Optimization Based on Particle Swarm Optimization and Genetic Algorithm

Ning Li

doi:doi:10.11648/j.ajmcm.20210604.14

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Research on Ship Collision Avoidance Path Optimization Based on Particle Swarm Optimization and Genetic Algorithm

Ning Li

Published in American Journal of Mathematical and Computer Modelling (Volume 6, Issue 4)

Received: 6 December 2021 Accepted: 13 December 2021 Published: 31 December 2021

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Abstract

With the increasingly busy shipping routes, ship collision accidents occur from time to time. In order to avoid ship collision, the research on ship collision avoidance decision has become a research hotspot. For a long time, many experts and scholars have been publishing research results on collision avoidance automation and artificial intelligence, in order to avoid or reduce ship collision accidents in the case of large marine traffic flow and complex traffic forms. Based on the previous research, considering the economic and safety requirements of ship collision avoidance, and based on particle swarm optimization algorithm, genetic algorithm and nonlinear programming theory, this paper establishes the optimization model of ship collision avoidance path planning. Combined with specific cases, the simulation analysis is carried out under the three collision avoidance situations of ship head-on, crossing and overtaking. The simulation results show that the convergence speed of particle swarm genetic hybrid optimization algorithm is fast, ship collision avoidance path is smooth, and path distance and steering angle is small. The optimal path of ship collision avoidance can meet the requirements of economy and safety at the same time, and the effectiveness and operation efficiency of the algorithm have been significantly improved.

Published in	American Journal of Mathematical and Computer Modelling (Volume 6, Issue 4)
DOI	10.11648/j.ajmcm.20210604.14
Page(s)	81-87
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Collision Avoidance, Path Optimization, Genetic Algorithm, Particle Swarm Optimization Algorithm

References

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[2]	ZHANG J, ZHANG D, YAN X, et al, 2015. A Distributed Anti-Collision Decision Support Formulation in Multi-Ship Encounter Situation Surface Craft [J]. Journal of Robotics. 336-348.
[3]	Besikci E B, Arslan O, Turan O, et al, 2016. An artificial neural network based decision support system for energy efficient ship operations [J]. Computers & Operations Research. 393-401.
[4]	Hu Jiaying, Liu Kezhong, Wu Xiaolie, Liu Jiongjiong, Yang Xing, 2020. An optimization method for ship collision avoidance decision-making based on prospect theory [J]. China Navigation. 18-23.
[5]	Liu H D, Sun R, Liu Q, 2019. The tactics of ship collision avoidance based on Quantum-behaved Wolf Pack Algorithm [J]. Concurrency and Computation: Practice and Experience. 31-38.
[6]	Zhang S K, Shi G Y, Liu Z J, et al, 2018. Data-driven based automatic maritime routing from massive AIS trajectories in the face of disparity [J]. Ocean Engineering. 240-250.
[7]	Huang Y M, Van Gelder P H A J M, 2020. Time-varying risk measurement for ship collision prevention [J]. Risk Analysis: An Official Publication of the Society for Risk Analysis. 82-88.
[8]	Zhang J F, Hu Q Y, Liao B J, 2019. Ship collision avoidance decision model and simulation based on collision circle [J]. TransNav: International Journal on Marine Navigation and Safety of Sea Transportation. 93-106.
[9]	Cheng Zhiyou, Li Yaling, Wu Bing, Richard S Tay, 2020. Early warning method and model of inland ship collision risk based on coordinated collision-avoidance actions [J]. Journal of Advanced Transportation. 72-78.
[10]	Guo, Zhang, Zheng, et al, 2020. An Autonomous Path Planning Model for Unmanned Ships Based on Deep Reinforcement Learning [J]. Sensors. 426.
[11]	Zhang Jiaqi, Fu Zhenkai, 2019. Overview of ship collision avoidance system research [J]. Science Technology and Engineering. 32-39.
[12]	Perera L P, 2012. Intelligent Ocean Navigation and Fuzzy-Bayesian Decision/Action Formulation [J]. IEEE Journal of Oceanic Engineering. 204-219.
[13]	Gil Mateusz, Wróbel Krzysztof, Montewka Jakub, 2019. Toward a method evaluating control actions in STPA-based model of ship-ship collision avoidance process [J]. Journal of Offshore Mechanics and Arctic Engineering. 52-58.
[14]	Thomas, 2007. Autonomous Ship Collision Avoidance Navigation Concepts, Technologies and Techniques [J]. Journal of Navigation. 129-142.
[15]	Perera L P, Ferrari V, Santos F P, et a1, 2014. Experimental Evaluations on Ship Autonomous Navigation and Collision Avoidance by Intelligent Guidance [J]. Oceanic Engineering. 116-125.
[16]	Z Wei, K Zhao, M We, 2015. Decision-Making in Ship Collision Avoidance based on Cat-Swarm Biological Algorithm [J]. International Conference on Computational Science and Numerical Algorithms. 114-122.

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Ning Li. (2021). Research on Ship Collision Avoidance Path Optimization Based on Particle Swarm Optimization and Genetic Algorithm. American Journal of Mathematical and Computer Modelling, 6(4), 81-87. https://doi.org/10.11648/j.ajmcm.20210604.14

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ACS Style

Ning Li. Research on Ship Collision Avoidance Path Optimization Based on Particle Swarm Optimization and Genetic Algorithm. Am. J. Math. Comput. Model. 2021, 6(4), 81-87. doi: 10.11648/j.ajmcm.20210604.14

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AMA Style

Ning Li. Research on Ship Collision Avoidance Path Optimization Based on Particle Swarm Optimization and Genetic Algorithm. Am J Math Comput Model. 2021;6(4):81-87. doi: 10.11648/j.ajmcm.20210604.14

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@article{10.11648/j.ajmcm.20210604.14,
  author = {Ning Li},
  title = {Research on Ship Collision Avoidance Path Optimization Based on Particle Swarm Optimization and Genetic Algorithm},
  journal = {American Journal of Mathematical and Computer Modelling},
  volume = {6},
  number = {4},
  pages = {81-87},
  doi = {10.11648/j.ajmcm.20210604.14},
  url = {https://doi.org/10.11648/j.ajmcm.20210604.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmcm.20210604.14},
  abstract = {With the increasingly busy shipping routes, ship collision accidents occur from time to time. In order to avoid ship collision, the research on ship collision avoidance decision has become a research hotspot. For a long time, many experts and scholars have been publishing research results on collision avoidance automation and artificial intelligence, in order to avoid or reduce ship collision accidents in the case of large marine traffic flow and complex traffic forms. Based on the previous research, considering the economic and safety requirements of ship collision avoidance, and based on particle swarm optimization algorithm, genetic algorithm and nonlinear programming theory, this paper establishes the optimization model of ship collision avoidance path planning. Combined with specific cases, the simulation analysis is carried out under the three collision avoidance situations of ship head-on, crossing and overtaking. The simulation results show that the convergence speed of particle swarm genetic hybrid optimization algorithm is fast, ship collision avoidance path is smooth, and path distance and steering angle is small. The optimal path of ship collision avoidance can meet the requirements of economy and safety at the same time, and the effectiveness and operation efficiency of the algorithm have been significantly improved.},
 year = {2021}
}

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TY  - JOUR
T1  - Research on Ship Collision Avoidance Path Optimization Based on Particle Swarm Optimization and Genetic Algorithm
AU  - Ning Li
Y1  - 2021/12/31
PY  - 2021
N1  - https://doi.org/10.11648/j.ajmcm.20210604.14
DO  - 10.11648/j.ajmcm.20210604.14
T2  - American Journal of Mathematical and Computer Modelling
JF  - American Journal of Mathematical and Computer Modelling
JO  - American Journal of Mathematical and Computer Modelling
SP  - 81
EP  - 87
PB  - Science Publishing Group
SN  - 2578-8280
UR  - https://doi.org/10.11648/j.ajmcm.20210604.14
AB  - With the increasingly busy shipping routes, ship collision accidents occur from time to time. In order to avoid ship collision, the research on ship collision avoidance decision has become a research hotspot. For a long time, many experts and scholars have been publishing research results on collision avoidance automation and artificial intelligence, in order to avoid or reduce ship collision accidents in the case of large marine traffic flow and complex traffic forms. Based on the previous research, considering the economic and safety requirements of ship collision avoidance, and based on particle swarm optimization algorithm, genetic algorithm and nonlinear programming theory, this paper establishes the optimization model of ship collision avoidance path planning. Combined with specific cases, the simulation analysis is carried out under the three collision avoidance situations of ship head-on, crossing and overtaking. The simulation results show that the convergence speed of particle swarm genetic hybrid optimization algorithm is fast, ship collision avoidance path is smooth, and path distance and steering angle is small. The optimal path of ship collision avoidance can meet the requirements of economy and safety at the same time, and the effectiveness and operation efficiency of the algorithm have been significantly improved.
VL  - 6
IS  - 4
ER  -

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Author Information

Ning Li

Department of Navigational Technology, Merchant Marine College, Shanghai Maritime University, Shanghai, P.R. China

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Plain Text BibTeX RIS

APA Style

Ning Li. (2021). Research on Ship Collision Avoidance Path Optimization Based on Particle Swarm Optimization and Genetic Algorithm. American Journal of Mathematical and Computer Modelling, 6(4), 81-87. https://doi.org/10.11648/j.ajmcm.20210604.14

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ACS Style

Ning Li. Research on Ship Collision Avoidance Path Optimization Based on Particle Swarm Optimization and Genetic Algorithm. Am. J. Math. Comput. Model. 2021, 6(4), 81-87. doi: 10.11648/j.ajmcm.20210604.14

Copy | Download

AMA Style

Ning Li. Research on Ship Collision Avoidance Path Optimization Based on Particle Swarm Optimization and Genetic Algorithm. Am J Math Comput Model. 2021;6(4):81-87. doi: 10.11648/j.ajmcm.20210604.14

Copy | Download

@article{10.11648/j.ajmcm.20210604.14,
  author = {Ning Li},
  title = {Research on Ship Collision Avoidance Path Optimization Based on Particle Swarm Optimization and Genetic Algorithm},
  journal = {American Journal of Mathematical and Computer Modelling},
  volume = {6},
  number = {4},
  pages = {81-87},
  doi = {10.11648/j.ajmcm.20210604.14},
  url = {https://doi.org/10.11648/j.ajmcm.20210604.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmcm.20210604.14},
  abstract = {With the increasingly busy shipping routes, ship collision accidents occur from time to time. In order to avoid ship collision, the research on ship collision avoidance decision has become a research hotspot. For a long time, many experts and scholars have been publishing research results on collision avoidance automation and artificial intelligence, in order to avoid or reduce ship collision accidents in the case of large marine traffic flow and complex traffic forms. Based on the previous research, considering the economic and safety requirements of ship collision avoidance, and based on particle swarm optimization algorithm, genetic algorithm and nonlinear programming theory, this paper establishes the optimization model of ship collision avoidance path planning. Combined with specific cases, the simulation analysis is carried out under the three collision avoidance situations of ship head-on, crossing and overtaking. The simulation results show that the convergence speed of particle swarm genetic hybrid optimization algorithm is fast, ship collision avoidance path is smooth, and path distance and steering angle is small. The optimal path of ship collision avoidance can meet the requirements of economy and safety at the same time, and the effectiveness and operation efficiency of the algorithm have been significantly improved.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Research on Ship Collision Avoidance Path Optimization Based on Particle Swarm Optimization and Genetic Algorithm
AU  - Ning Li
Y1  - 2021/12/31
PY  - 2021
N1  - https://doi.org/10.11648/j.ajmcm.20210604.14
DO  - 10.11648/j.ajmcm.20210604.14
T2  - American Journal of Mathematical and Computer Modelling
JF  - American Journal of Mathematical and Computer Modelling
JO  - American Journal of Mathematical and Computer Modelling
SP  - 81
EP  - 87
PB  - Science Publishing Group
SN  - 2578-8280
UR  - https://doi.org/10.11648/j.ajmcm.20210604.14
AB  - With the increasingly busy shipping routes, ship collision accidents occur from time to time. In order to avoid ship collision, the research on ship collision avoidance decision has become a research hotspot. For a long time, many experts and scholars have been publishing research results on collision avoidance automation and artificial intelligence, in order to avoid or reduce ship collision accidents in the case of large marine traffic flow and complex traffic forms. Based on the previous research, considering the economic and safety requirements of ship collision avoidance, and based on particle swarm optimization algorithm, genetic algorithm and nonlinear programming theory, this paper establishes the optimization model of ship collision avoidance path planning. Combined with specific cases, the simulation analysis is carried out under the three collision avoidance situations of ship head-on, crossing and overtaking. The simulation results show that the convergence speed of particle swarm genetic hybrid optimization algorithm is fast, ship collision avoidance path is smooth, and path distance and steering angle is small. The optimal path of ship collision avoidance can meet the requirements of economy and safety at the same time, and the effectiveness and operation efficiency of the algorithm have been significantly improved.
VL  - 6
IS  - 4
ER  -

Copy | Download