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Cluster Computing

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22.01.2021

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

verfasst von: Xiao-huan Liu, Degan Zhang, Jie Zhang, Ting Zhang, Haoli Zhu

Erschienen in: Cluster Computing | Ausgabe 3/2021

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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.

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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/s10586-020-03055-9CrossRef

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)CrossRef

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.2019.294300CrossRef

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)CrossRef

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

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)CrossRef

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)CrossRef

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)

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)CrossRef

10.

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

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)

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.2019.2950266CrossRef

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)CrossRef

14.

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

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)CrossRef

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.2020.153134CrossRef

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-zCrossRef

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/s10586-018-1845-4CrossRef

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.2020.2998137CrossRef

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)MathSciNetCrossRef

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-xCrossRef

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/s10586-017-1538-4CrossRef

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/s10586-020-03112-3CrossRef

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-xCrossRef

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.2019.2939423CrossRef

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)CrossRef

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)CrossRef

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-yCrossRef

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)CrossRef

30.

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

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)CrossRef

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)CrossRef

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/s110360181123yCrossRef

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)

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

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)CrossRef

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)CrossRef

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)CrossRef

39.

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

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)CrossRef

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)CrossRef

42.

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

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.2019.00031CrossRef

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)

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)CrossRef

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)

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)

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)

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)

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)

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)

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)MathSciNetCrossRef

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.0226204CrossRef

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/s10586-018-2554-8CrossRef

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-wCrossRef

Titel: A path planning method based on the particle swarm optimization trained fuzzy neural network algorithm
verfasst von: Xiao-huan Liu
Degan Zhang
Jie Zhang
Ting Zhang
Haoli Zhu
Publikationsdatum: 22.01.2021
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe 3/2021
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-021-03235-1

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