Skip to main content
SpringerLink
Log in

A multi-subpopulation bacterial foraging optimisation algorithm with deletion and immigration strategies for unmanned surface vehicle path planning

  • Original Research Paper
  • Published:
Intelligent Service Robotics Aims and scope Submit manuscript

Abstract

With the advantages of flexible control ability, unmanned surface vehicle (USV) has been widely applied in civil and military fields. A number of researchers have been working on the development of intelligent path planning algorithms to plan a high-quality and collision-free path which is applied to guide USV through cluttered environments. The conventional algorithms may either have issues with trapping into a local optimal solution or face a slow convergence problem. This paper presents a novel multi-subpopulation bacterial foraging optimisation (MS-BFO) algorithm for USV path planning that enhances the searching performance, especially, in a complex environment. This method constructs the deletion and immigration strategies (DIS), which guarantees the elite optimised individual of each subpopulation to be inherited by others, thus to consequently lead to fast convergence speed. The experimental results show that the proposed method is able to suggest an optimised path within the shortest length of time, compared with other optimisation algorithms.

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

Access this article

Log in via an institution

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
Fig. 15

Similar content being viewed by others

References

  1. Hou Y, Kang K, Xiong Y, Liang X, Su L (2020) Uncertainty optimisation design of USV based on the six sigma method. Ocean Eng 200:107045

    Article  Google Scholar 

  2. Jimenez JF, Giron-Sierra JM (2020) USV based automatic deployment of booms along quayside mooring ships: scaled experiments and simulations. Ocean Eng 207:107438

    Article  Google Scholar 

  3. Long Y, Zuo Z, Su Y, Li J, Zhang H (2020) An a*-based bacterial foraging optimisation algorithm for global path planning of unmanned surface vehicles. J Navig 73(6):1247–1262

    Article  Google Scholar 

  4. Li J, Shum HP, Fu X, Sexton G, Yang L (2016) Experience-based rule base generation and adaptation for fuzzy interpolation. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp 102–109. IEEE

  5. Yang L, Zuo Z, Chao F, Qu Y, Ramakrishnan S (2017) Fuzzy interpolation systems and applications. Modern fuzzy control systems and its applications pp 49–70

  6. Zuo Z, Li J, Anderson P, Yang L, Naik N (2018) Grooming detection using fuzzy-rough feature selection and text classification. In: 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp 1–8. IEEE

  7. Li J, Yang L, Fu X, Chao F, Qu Y (2018) Interval type-2 tsk+ fuzzy inference system. In: 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp 1–8. IEEE

  8. Elisa N, Li J, Zuo Z, Yang L (2018) Dendritic cell algorithm with fuzzy inference system for input signal generation. In: UK workshop on computational intelligence, pp 203–214. Springer

  9. Zuo Z, Li J, Yang L (2019) Curvature-based sparse rule base generation for fuzzy interpolation using menger curvature. In: UK Workshop on Computational Intelligence, pp 53–65. Springer

  10. Li J, Yang L, Qu Y, Sexton G (2018) An extended Takagi–Sugeno–Kang inference system (tsk+) with fuzzy interpolation and its rule base generation. Soft Comput 22(10):3155–3170

    Article  Google Scholar 

  11. Zuo Z, Li J, Wei B, Yang L, Chao F, Naik N (2019) Adaptive activation function generation for artificial neural networks through fuzzy inference with application in grooming text categorisation. In: 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp 1–6. IEEE

  12. Singh Y, Sharma S, Sutton R, Hatton D (2018) Towards use of Dijkstra algorithm for optimal navigation of an unmanned surface vehicle in a real-time marine environment with results from artificial potential field. Int J Mar Navig Saf Sea Transp 12(1):2640

    Google Scholar 

  13. Yang L, Li J, Chao F, Hackney P, Flanagan M (2018) Job shop planning and scheduling for manufacturers with manual operations. Expert Systems p e12315

  14. Chen P, Li Q, Zhang C, Cui J, Zhou H (2019) Hybrid chaos-based particle swarm optimization-ant colony optimization algorithm with asynchronous pheromone updating strategy for path planning of landfill inspection robots. Int J Adv Robot Syst 16(4):1729881419859083

    Google Scholar 

  15. Cameron R, Zuo Z, Sexton G, Yang L (2017) A fall detection/recognition system and an empirical study of gradient-based feature extraction approaches. In: UK Workshop on Computational Intelligence, pp 276–289. Springer

  16. Zuo Z, Yang L, Peng Y, Chao F, Qu Y (2018) Gaze-informed egocentric action recognition for memory aid systems. IEEE Access 6:12894–12904

    Article  Google Scholar 

  17. Li J, Qu Y, Chao F, Shum H, Ho E, Yang L (2019) Machine learning algorithms for network intrusion detection. Springer international publishing

  18. Beheshti Z, Shamsuddin SMH (2014) Capso: centripetal accelerated particle swarm optimization. Inf Sci 258:54–79

    Article  MathSciNet  Google Scholar 

  19. Shi BH, Su YX, Zhang HJ, Liu JW, Wan LL (2019) Obstacles modeling method in cluttered environments using satellite images and its application to path planning for USV. Int J Nav Arch Ocean Eng 11(1):202–210

    Article  Google Scholar 

  20. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: IEEE Congress on Evolutionary Computation, pp 4661–4667

  21. Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22(3):52–67

    Article  Google Scholar 

  22. Chi R, Su Y, Qu Z, Chi X (2019) A hybridization of cuckoo search and differential evolution for the logistics distribution center location problem. Mathematical Problems in Engineering

  23. Guo L, Meng Z, Sun Y, Wang L (2016) Parameter identification and sensitivity analysis of solar cell models with cat swarm optimization algorithm. Energy Convers Manag 108:520–528

    Article  Google Scholar 

  24. Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98. https://doi.org/10.1016/j.advengsoft.2015.01.010

    Article  Google Scholar 

  25. Song R, Liu Y, Bucknall R (2019) Smoothed a* algorithm for practical unmanned surface vehicle path planning. Appl Ocean Res 83:9–20

    Article  Google Scholar 

  26. Yuan-hui W, Cen C (2016) Research on optimal planning method of USV for complex obstacles. In: 2016 IEEE International Conference on Mechatronics and Automation, pp 2507–2511. IEEE

  27. Nseef SK, Abdullah S, Turky A, Kendall G (2016) An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems. Knowl-Based Syst 104:14–23

    Article  Google Scholar 

  28. Bai X, Yan W, Ge SS, Cao M (2018) An integrated multi-population genetic algorithm for multi-vehicle task assignment in a drift field. Inf Sci 453:227–238

    Article  Google Scholar 

  29. Liang JJ, Song H, Qu BY, Mao XB (2012) Path planning based on dynamic multi-swarm particle swarm optimizer with crossover. In: International Conference on Intelligent Computing, pp 159–166. Springer

  30. Huo CL, Lai TY, Sun TY (2011) The preliminary study on multi-swarm sharing particle swarm optimization: Applied to uav path planning problem. In: 2011 IEEE Congress of Evolutionary Computation (CEC), pp 1770–1776. IEEE

  31. Gutierrez A, Dieulle L, Labadie N, Velasco N (2016) A multi population memetic algorithm for the vehicle routing problem with time windows and stochastic travel and service times. IFAC-PapersOnLine 49(12):1204–1209

    Article  Google Scholar 

  32. Yuh J, Marani G, Blidberg DR (2011) Applications of marine robotic vehicles. Intell Serv Robot 4(4):221

    Article  Google Scholar 

  33. Xin J, Zhong J, Yang F, Cui Y, Sheng J (2019) An improved genetic algorithm for path-planning of unmanned surface vehicle. Sensors 19(11):2640

    Article  Google Scholar 

  34. Yang W, Yuan Y, Ren W, Liu J, Scheirer WJ, Wang Z, Zhang T, Zhong Q, Xie D, Pu S et al (2020) Advancing image understanding in poor visibility environments: a collective benchmark study. IEEE Trans Image Process 29:5737–5752

    Google Scholar 

  35. Zuo Z, Yang L, Liu Y, Chao F, Song R, Qu Y (2019) Histogram of fuzzy local spatio-temporal descriptors for video action recognition. IEEE Trans Ind Inf 16(6):4059–4067

    Article  Google Scholar 

Download references

Acknowledgements

The research of this paper is supported by the Scientific Research Program of Education Department of Hubei Province, China (B2020360).

Author information

Authors and Affiliations

  1. School of Automation, Wuhan University of Technology, Wuhan, China

    Yang Long, Yixin Su & Binghua Shi

  2. Department of Computer Science, Durham University, Durham, UK

    Zheming Zuo

  3. School of Computing and Digital Technologies, Teesside University, Middlesbrough, UK

    Jie Li

  4. Science and Technology College of Hubei Minzu University, Enshi, China

    Yang Long

Authors
  1. Yang Long
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yixin Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Binghua Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zheming Zuo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jie Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yixin Su.

Ethics declarations

Conflict of interest

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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

Long, Y., Su, Y., Shi, B. et al. A multi-subpopulation bacterial foraging optimisation algorithm with deletion and immigration strategies for unmanned surface vehicle path planning. Intel Serv Robotics 14, 303–312 (2021). https://doi.org/10.1007/s11370-021-00361-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11370-021-00361-y

Keywords

Search

Navigation