Skip to main content

Advertisement

SpringerLink
Log in

A path planning method based on the particle swarm optimization trained fuzzy neural network algorithm

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

The basic fuzzy neural network algorithm has slow convergence and large amount of calculation, so this paper designed a particle swarm optimization trained fuzzy neural network algorithm to solve this problem. Traditional particle swarm optimization is easy to fall into local extremes and has low efficiency, this paper designed new update rules for inertia weight and learning factors to overcome these problems. We also designed training rules for the improved particle swarm optimization to train fuzzy neural network, and the hybrid algorithm is applied to solve the path planning problem of intelligent driving vehicles. The efficiency and practicability of the algorithm are proved by experiments.

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
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24

Similar content being viewed by others

References

  1. Liu, Z.X., Zhang, D.G., Luo, G.Z., et al.: A new method of emotional analysis based on CNN-BiLSTM hybrid neural network. Cluster Comput. 23(1), 2901–2913 (2020). https://doi.org/10.1007/s10586-020-03055-9

    Article  Google Scholar 

  2. Duan, P.B., Mao, G.Q., Liang, W.: A unified spatio- temporal model for short-term traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 20(9), 3212–3223 (2019)

    Article  Google Scholar 

  3. Luo, M., Hou, X.R., Yang, S.X.: A multi-scale map method based on bioinspired neural network algorithm for robot path planning. IEEE Access. 7(1), 142682–142691 (2019). https://doi.org/10.1109/ACCESS.2019.294300

    Article  Google Scholar 

  4. Zhang, D.G., Tang, Y.M.: Novel reliable routing method for engineering of internet of vehicles based on graph theory. Eng. Comput. 36(1), 226–247 (2019)

    Article  Google Scholar 

  5. Sheng, Y.H., Mei, X.H.: Uncertain random shortest path problem. Soft Comput 24(4), 2431–2440 (2020)

    Article  MathSciNet  Google Scholar 

  6. Li, G., Zheng, K.: An energy-balanced routing method based on forward-aware factor for Wireless Sensor Network. IEEE Trans. Industr. Inf. 10(1), 766–773 (2014)

    Article  Google Scholar 

  7. Zhang, D.G., Ge, H., Zhang, T., et al.: New multi-hop clustering algorithm for vehicular Ad Hoc networks. IEEE Trans. Intell. Transp. Syst. 20(4), 1517–1530 (2019)

    Article  Google Scholar 

  8. Zhang, R.L., Zhang, Y.T., Zheng, Z.P.: Parametrical optimization of particle dampers based on particle swarm algorithm. Appl. Acoust. 11(8), 160–164 (2020)

    Google Scholar 

  9. Zhang, T., Dong, Y.: Novel optimized link state routing protocol based on quantum genetic strategy for mobile learning. J. Netw. Comput. Appl. 122(1), 37–49 (2018)

    Article  Google Scholar 

  10. Zhang, D.G., Niu, H.L., Liu, S.: Novel PEECR-based clustering routing approach. Soft. Comput. 21(24), 7313–7323 (2017)

    Article  Google Scholar 

  11. Gao, J.X., Liu, X.H., Zhang, T.: Novel approach of distributed & adaptive trust metrics for MANET. Wireless Netw. 3(1), 1–17 (2019)

    Google Scholar 

  12. Zhao, P.Z., Cui, Y.Y., Chen, L.: A new method of mobile Ad Hoc network routing based on greed forwarding improvement strategy. IEEE Access. 7(1), 158514–158524 (2019). https://doi.org/10.1109/ACCESS.2019.2950266

    Article  Google Scholar 

  13. Wang, X., Song, X.D.: New medical image fusion approach with coding based on SCD in wireless sensor network. J. Electr. Eng. Technol. 10(6), 2384–2392 (2015)

    Article  Google Scholar 

  14. Zhang, J., Xia, Y.Q.: A novel learning-based global path planning algorithm for planetary rovers. Neurocomputing 13(9), 361–365 (2019)

    Google Scholar 

  15. Gong, C.L., Jiang, K.W.: A kind of new method of intelligent trust engineering metrics (ITEM) for application of mobile Ad Hoc network. Eng. Comput. 37(5), 1617–1643 (2019)

    Article  Google Scholar 

  16. Cui, Y.Y., Zhang, T., Chen, L.: Novel method of mobile edge computation offloading based on evolutionary game strategy for IoT devices. AEU-Int. J. Electron. Commun. 118(5), 1–13 (2020). https://doi.org/10.1016/j.aeue.2020.153134

    Article  Google Scholar 

  17. Feng, X., Gong, P., Jin, P.: Supply chain scheduling optimization based on genetic particle swarm optimization algorithm. Cluster Comput. 22(1), 14767–14775 (2019). https://doi.org/10.1007/s10586-018-2400-z

    Article  Google Scholar 

  18. Bi, Y., Xiang, M., Florian, S.: A simplified and efficient particle swarm optimization algorithm considering particle diversity. Cluster Comput. 22(1), 13273–13282 (2019). https://doi.org/10.1007/s10586-018-1845-4

    Article  Google Scholar 

  19. Han, G.J., Zhou, Z.R., Zhang, T.W.: Ant-Colony-Based complete-coverage path-planning algorithm for underwater gliders in ocean areas with thermoclines. IEEE Trans. Veh Technol. 69(8), 8959–8971 (2020). https://doi.org/10.1109/TVT.2020.2998137

    Article  Google Scholar 

  20. Wang, X., Song, X.D.: A novel approach to mapped correlation of ID for RFID anti-collision. IEEE Trans. Serv. Comput. 7(4), 741–748 (2014)

    Article  MathSciNet  Google Scholar 

  21. Wang, F., Chao, Z.Q., Dhivya, R.: Edge detection of satellite image using fuzzy logic. Cluster Comput. 22(1), 11891–11898 (2019). https://doi.org/10.1007/s10586-017-1508-x

    Article  Google Scholar 

  22. Huang, L.B., Li, H.Y., Zhang, C.Q.: Trajectory tracking control of robot manipulator based on RBF neural network and fuzzy sliding mode. Cluster Comput. 22(1), 5799–5809 (2019). https://doi.org/10.1007/s10586-017-1538-4

    Article  Google Scholar 

  23. Mohamed, H., Ahmed, E.Y.: Teeth infection and fatigue prediction using optimized neural networks and big data analytic too. Cluster Comput. 23(1), 1669–1682 (2020). https://doi.org/10.1007/s10586-020-03112-3

    Article  Google Scholar 

  24. Wang, J.F., Zhong, T., Zhou, H.M.: Fuzzy neural network model construction based on shortest path parallel algorithm. Cluster Comput. 22(1), 3413–3418 (2019). https://doi.org/10.1007/s10586-018-2188-x

    Article  Google Scholar 

  25. Liu, X.H., Zhang, D.G., Yan, H.R., et al.: A new algorithm of the best path selection based on machine learning. IEEE Access. 7(1), 126913–126928 (2019). https://doi.org/10.1109/ACCESS.2019.2939423

    Article  Google Scholar 

  26. Chen, J.Q., Mao, G.Q.: Capacity of cooperative vehicular networks with infrastructure support: multi-user case. IEEE Trans. Veh. Technol. 67(2), 1546–1560 (2018)

    Article  Google Scholar 

  27. Chen, J.Q., Guo, M., Li, C.L.: A topological approach to secure message dissemination in vehicular networks. IEEE Trans. Intell. Transp. Syst. 21(1), 135–148 (2020)

    Article  Google Scholar 

  28. Wu, H., Zhao, P.Z.: New approach of multi-path reliable transmission for marginal wireless sensor network. Wireless Netw. 12(1), 1–15 (2019). https://doi.org/10.1007/s11276-019-02216-y

    Article  Google Scholar 

  29. Zhang, D.G., Liu, X.H., Cui, Y.Y.: A kind of novel RSAR protocol for mobile vehicular Ad hoc network. CCF Trans. Netw. 2(2), 111–125 (2019)

    Article  Google Scholar 

  30. Zhang, D.G., Zhang, T.: Novel self-adaptive routing service algorithm for application of VANET. Appl Intell. 49(5), 1866–1879 (2019)

    Article  Google Scholar 

  31. Zheng, K., Zhang, T.: A novel multicast routing method with minimum transmission for WSN of cloud computing service. Soft. Comput. 19(7), 1817–1827 (2015)

    Article  Google Scholar 

  32. Zhang, T.: A kind of novel method of power allocation with limited cross-tier interference for CRN. IEEE Access. 7(1), 82571–82583 (2019)

    Article  Google Scholar 

  33. Chen, C., Cui, Y.Y.: New method of energy efficient subcarrier allocation based on evolutionary game theory. Mobile Netw. Appl. 9(1), 1–15 (2018). https://doi.org/10.1007/s110360181123y

    Article  Google Scholar 

  34. Liu, S., Liu, X.H., Zhang, T.: Novel dynamic source routing protocol (DSR) based on genetic algorithm-bacterial foraging optimization (GA-BFO). Int. J. Commun. Syst. 31(18), 1–20 (2018)

    Google Scholar 

  35. Wang, X., Song, X.D.: New clustering routing method based on PECE for WSN. EURASIP J. Wirel. Commun. Netw. 162(1), 1–13 (2015)

    MathSciNet  Google Scholar 

  36. Zhou, S., Tang, Y.M.: A low duty cycle efficient MAC protocol based on self-adaption and predictive strategy. Mobile Netw. Appl. 23(4), 828–839 (2018)

    Article  Google Scholar 

  37. Liu, S., Zhang, D.G., Liu, X.H.: Dynamic analysis for the average shortest path length of mobile Ad Hoc networks under random failure scenarios. IEEE Access. 7(1), 21343–21358 (2019)

    Article  Google Scholar 

  38. Zhang, X.D.: Design and implementation of embedded un-interruptible power supply system (EUPSS) for web-based mobile application. Enterp. Inform. Syst. 6(4), 473–489 (2012)

    Article  Google Scholar 

  39. Zheng, K., Zhao, D.X.: Novel quick start (QS) method for optimization of TCP. Wirel. Netw. 22(1), 211–222 (2016)

    Article  Google Scholar 

  40. Zhu, Y.N., Zhao, P.Z., Dai, W.B.: A new constructing approach for a weighted topology of wireless sensor networks based on local-world theory for the Internet of Things (IOT). Comput. Math. Appl. 64(5), 1044–1055 (2012)

    Article  Google Scholar 

  41. Zhang, D.G., Liu, S., Zhang, T.: Novel unequal clustering routing protocol considering energy balancing based on network partition & distance for mobile education. J. Netw. Comput. Appl. 88(15), 1–9 (2017)

    Article  Google Scholar 

  42. Zhang, D.G.: A new approach and system for attentive mobile learning based on seamless migration. Appl. Intell. 36(1), 75–89 (2012)

    Article  Google Scholar 

  43. Liu, X.H., Zhang, D.G., Zhang, T., et al.: Novel approach of the best path selection based on prior knowledge reinforcement learning. In: 2019 IEEE International Conference on Smart Internet of Things (SmartIoT), pp. 148–154. IEEE, Tianjin (2019). https://doi.org/10.1109/SmartIoT.2019.00031

    Chapter  Google Scholar 

  44. Zhang, T., Zhang, J.: A kind of effective data aggregating method based on compressive sensing for wireless sensor network. EURASIP J. Wirel. Commun. Netw. 159(1), 1–15 (2018)

    Google Scholar 

  45. Liu, S., Zhang, D.G., et al.: Adaptive repair algorithm for TORA routing protocol based on flood control strategy. Comput. Commun. 151(1), 437–448 (2020)

    Article  Google Scholar 

  46. Cui, Y.Y.: Novel method of mobile edge computation of loading based on evolutionary game strategy for IoT devices. AEU-Int. J. Electron. Commun. 118(5), 1–13 (2020)

    Google Scholar 

  47. Chen, L., Zhang, J., Chen, J.: A multi-path routing protocol based on link lifetime and energy consumption prediction for mobile edge computing. IEEE Access. 8(1), 69058–69071 (2020)

    Google Scholar 

  48. Tian, J., Sun, C.L., Tan, Y., et al.: Granularity- based surrogate- assisted particle swarm optimization for high- dimensional expensive optimization. Knowl.-Based Syst. 4(2), 187 (2020)

    Google Scholar 

  49. Piao, M.J., Zhang, J.: New algorithm of multi-strategy channel allocation for edge computing. AEUE-Int. J. Electron. Commun. 126(11), 1–15 (2020)

    Google Scholar 

  50. Zhang, T., Zhang, D.G., Yan, H.R.: A new method of data missing estimation with FNN-based tensor heterogeneous ensemble learning for internet of vehicle. Neurocomputing. 9(1), 1–15 (2020)

    Google Scholar 

  51. Wang, J.X., Fan, H.R.: New method of traffic flow forecasting based on quantum particle swarm optimization strategy for intelligent transportation system. Int. J. Commun Syst 33(10), 1–13 (2020)

    Google Scholar 

  52. Martins, L.D., Hirsch, P., Juan, A.: Agile optimization of a two-echelon vehicle routing problem with pickup and delivery. Int. Trans. Op. Res. 28(1), 201–221 (2020)

    Article  MathSciNet  Google Scholar 

  53. Fan, Y.W., Wang, G., Lu, X.L.: Distributed forecasting and ant colony optimization for the bike-sharing rebalancing problem with unserved demands. PLoS ONE 14(12), e0226204 (2019). https://doi.org/10.1371/journal.pone.0226204

    Article  Google Scholar 

  54. Chen, W., Chen, Z.Y., Liu, J., et al.: A novel shortest path query algorithm. Cluster Computing. 22(1), 6729–6740 (2019). https://doi.org/10.1007/s10586-018-2554-8

    Article  Google Scholar 

  55. Mohammad, K., Sepehrifar, A.F., Mohammad, B.S.: Shortest path computation in a network with multiple destinations. Arab. J. Sci. Eng. 45(1), 3223–3231 (2020). https://doi.org/10.1007/s13369-020-04340-w

    Article  Google Scholar 

Download references

Acknowledgements

This study was funded by the following funds, and I would like to express my gratitude: National Natural Science Foundation of China (Grant No. 61571328); Tianjin Key Natural Science Foundation Project (18JCZDJC96800); Tianjin Science and Technology Major Project (15ZXDSGX 00050); Tianjin Science and Technology Innovation Team Fund projects (TD12-5016, TD13-5025, TD2015-23); Tianjin Science and Technology Service Industry Major Science and Technology Project (16ZXFWGX00010, 17YFZCGX00360).

Author information

Authors and Affiliations

  1. Key Laboratory of Computer Vision and System, Tianjin University of Technology, Ministry of Education, Tianjin, 300384, China

    Xiao-huan Liu, Degan Zhang, Ting Zhang & Haoli Zhu

  2. Tianjin Key Lab of Intelligent Computing & Novel Software Technology, Tianjin University of Technology, Tianjin, 300384, China

    Xiao-huan Liu, Degan Zhang, Ting Zhang & Haoli Zhu

  3. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Jie Zhang

Authors
  1. Xiao-huan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Degan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jie Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ting Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Haoli Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Xiao-huan Liu or Degan Zhang.

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

Liu, Xh., Zhang, D., Zhang, J. et al. A path planning method based on the particle swarm optimization trained fuzzy neural network algorithm. Cluster Comput 24, 1901–1915 (2021). https://doi.org/10.1007/s10586-021-03235-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-021-03235-1

Keywords

Search

Navigation