Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

Double ant colony algorithm based on dynamic feedback for energy-saving route planning for ships

  • Foundations
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

To realize an accurate and efficient route planning for ships in a complex marine environment, the novel double ant colony algorithm (NDACA) based on dynamic feedback is proposed in this work. The planning process to identify the lowest energy consumption route is used as an example to introduce the applications of the proposed algorithm. First, the energy consumption model is established by analyzing the ship's motion, which is used in the pheromone updating strategy. Next, based on the energy consumption information of the route, the ant colony is divided into exploratory and optimized ants. Using a closed-loop feedback strategy, the number of ants in each colony is continuously adjusted, which ensures the solution quality and speed of convergence of the algorithm. Simulations in the working attribute environment show that NDACA has an all-round good performance. Compared with other algorithms, NDACA can plan a more energy-saving route while considering the influence of marine environmental factors, which has great practical significance for ship operations management.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Chen W, Yao TR, Hong JX (2002) Research on an isochrone approach and its shortest routing algorithms based on GIS. J Wuhan Univ Technol 04:466–469

    Google Scholar 

  • Cheng CB, Mao CP (2007) A modified ant colony system for solving the travelling salesman problem with time windows. Math Comput Modell 46:1225–1235

    Article  MathSciNet  Google Scholar 

  • Dorigo M, Maria L (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1997):53–66

    Article  Google Scholar 

  • DTU Cognitive Systems Group (2011) Propulsion modeling. http://cogsys.imm.dtu.dk/propulsionmodelling/ index.html

  • Fagerholt K, Laporte G, Norstad I (2010) Reducing fuel emissions by optimizing speed on shipping routes. J Oper Res Soc 61(3)

  • Fossen TI (2011) Handbook of marine craft hydrodynamics and motion control. Wiley, New York

    Book  Google Scholar 

  • Gambardella LM, Dorigo M (2000) A reinforcement learning approach to traveling salesman problem. Mach Learn Proc 170(3):252–260

    Google Scholar 

  • Han J, Tian Y (2008) An improved ant colony optimization algorithm based on dynamic control of solution construction and mergence of local search solutions. In: Fourth international conference on natural computation (Icnc’08), vol 7, pp 490–495

  • Hansen H, Freund M (2010) Assistance tools for operational fuel efficiency. In: 9th conference computer and IT applications in the maritime industries (COMPIT), pp 356–366

  • Huang C, Fei JY, Liu Y (2017) Smooth path planning method based on dynamic feedback A ~ * ant colony algorithm. Trans Chin Soc Agric Mach 48(4):34–40

    Google Scholar 

  • James J, Corbett, Wang HF, James J et al. (2009) The effectiveness and costs of speed reductions on emissions from international shipping. Transport Res Part D 14(8)

  • Liu CA, Yan XH, Liu CY et al (2011) Dynamic path planning for mobile robot based on improved ant colony optimization algorithm. Acta Electronica Sinica 39(5):1220–1224

    Google Scholar 

  • Ma RQ, Huang LZ, Wei MS et al (2018) Intelligent speed optimization of fixed route ship based on real ship monitoring data. J Dalian Mar Univ 44(01):31–35

    Google Scholar 

  • Meng XD, Yuan ZX (2016) A minimum fuel consumption speed model considering effect of irregular wind and wave. J Shanghai Mar Univ 37(1):19–24

    Google Scholar 

  • Mou XH (2017) Research on the optimization of economical route for sea vessels based on dynamic programming algorithm. Wuhan University of Technology, Wuhan

    Google Scholar 

  • Nicolas B, Dimitris K (2016). On the estimation of ship's fuel consumption and speed curve: a statistical approach. J Ocean Eng Sci 1(2)

  • Park J, Kim N (2014) A comparison and analysis of ship optimal routing scenarios considering ocean environment. J Soc Naval Archit Korea 51:99–106

    Article  Google Scholar 

  • Roh MI (2013) Determination of an economical ship route considering the effects of sea state for lower fuel consumption. J Naval Archit Ocean Eng 5:246–262

    Article  Google Scholar 

  • Seçkiner SU, Eroglu Y, Emrullah M, Dereli T (2013) Ant colony optimization for continuous functions by using novel pheromone updating. Appl Math Comput 219(9):4163–4175

    MathSciNet  MATH  Google Scholar 

  • Sim KM, Sun WH (2002) Multiple ant-colony optimization for network routing. In: Proceedings of the 1st international symposium on cyber worlds (Tokyo). IEEE Computer Society, pp 277–281

  • Stutzle T, Hoos H (2002) MAX-MIN ant system and local search for the traveling salesman problem. In: IEEE international conference on evolutionary computation. IEEE, pp 309–314

  • Takashima K, Mezaoui B, Shoji R (2009) On the fuel saving operation for coastal merchant ships using weather routing. TransNAV 3(4):401–406

    Google Scholar 

  • Wang HJ, Xu J, Zhao H et al (2017) Path planning of greenhouse robot based on potential field ant colony algorithm. Jiangsu Agric Sci 45(18):222–225

    Google Scholar 

  • Wit CD (1990) Proposal for low cost ocean weather routing. J Navig 43(3):428–439

    Article  Google Scholar 

  • Xia GQ, Han ZW, Zhao B (2019) Unmanned surface vessel path planning based on quantum ant algorithm. J Harbin Eng Univ 40(7):1263–1268

    Google Scholar 

  • Xv KB, Lu XY, Huang Y et al (2019) Robot path planning based on double-layer ant colony optimization algorithm and dynamic environment. Acta Electronica Sinica 47(10):2166–2176

    Google Scholar 

  • Yang K, You XM, Liu S (2019) Self-adaptive double-population ant colony algorithm with entropy. Comput Eng Appl 55(19):66–73

    Google Scholar 

  • Yu ZW, Ye Q (2007) Cause analysis of improved algorithm of shortcut on selecting the favorite line. Ship Ocean Eng 06:86–88

    Google Scholar 

  • Yuan J, Wang H, Lin C, Liu D, Yu D (2019) A novel GRU-RNN network model for dynamic path planning of mobile robot. IEEE Access 7:15140–15151

    Article  Google Scholar 

Download references

Acknowledgements

This work was support by Zhejiang Provincial Natural Science Foundation of China under Grant No. LY20E090002, and Zhoushan City Science and Technology Planned Project under Grant No. 2018C21018.

Author information

Authors and Affiliations

  1. Zhejiang Ocean University, Zhoushan, 316022, China

    Liangxiong Dong, Jun Li, Wei Xia & Qiang Yuan

Authors
  1. Liangxiong Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qiang Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liangxiong Dong.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dong, L., Li, J., Xia, W. et al. Double ant colony algorithm based on dynamic feedback for energy-saving route planning for ships. Soft Comput 25, 5021–5035 (2021). https://doi.org/10.1007/s00500-021-05683-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-021-05683-8

Keywords

Search

Navigation