Skip to main content
SpringerLink
Log in

Improved CS Algorithm and its Application in Parking Space Prediction

  • Published:
Journal of Bionic Engineering Aims and scope Submit manuscript

Abstract

This paper simulates the cuckoo incubation process and flight path to optimize the Wavelet Neural Network (WNN) model, and proposes a parking prediction algorithm based on WNN and improved Cuckoo Search (CS) algorithm. First, the initialization parameters are provided to optimize the WNN using the improved CS. The traditional CS algorithm adopts the strategy of overall update and evaluation, but does not consider its own information, so the convergence speed is very slow. The proposed algorithm employs the evaluation strategy of group update, which not only retains the advantage of fast convergence of the dimension-by-dimension update evaluation strategy, but also increases the mutual relationship between the nests and reduces the overall running time. Then, we use the WNN model to predict parking information. The proposed algorithm is compared with six different heuristic algorithms in five experiments. The experimental results show that the proposed algorithm is superior to other algorithms in terms of running time and accuracy.

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

Similar content being viewed by others

References

  1. Wang G G, Guo L H, Wang H Q, Duan H, Liu L, Li J. Incorporating mutation scheme into krill herd algorithm for global numerical optimization. Neural Computing and Applications, 2014, 24, 853–871.

    Article  Google Scholar 

  2. Wang G G, Deb S, Gandomi A H, Alavi A. Opposition-based krill herd algorithm with Cauchy mutation and position clamping. Neurocomputing, 2016, 177, 147–157.

    Article  Google Scholar 

  3. Wang G G, Gandomi A H, Alavi A H. Stud krill herd algorithm. Neurocomputing, 2014, 128, 363–370.

    Article  Google Scholar 

  4. Wang G G, Guo L, Gandomi A H, Hao G, Wang H. Chaotic krill herd algorithm. Information Sciences, 2014, 274, 17–34.

    Article  MathSciNet  Google Scholar 

  5. Wang G G, Gandomi A H, Yang X S, Alavi A. A new hybrid method based on krill herd and cuckoo search for global optimisation tasks. International Journal of Bio-inspired Computation, 2016, 8, 286–299.

    Article  Google Scholar 

  6. Wang G G, Gandomi A H, Alavi A H. An effective krill herd algorithm with migration operator in biogeography-based optimization. Applied Mathematical Modelling, 2014, 38, 2454–2462.

    Article  MathSciNet  Google Scholar 

  7. Wang H, Yi J H. An improved optimization method based on krill herd and artificial bee colony with information exchange. Memetic Computing, 2018, 10, 177–198.

    Article  Google Scholar 

  8. Rizk-Allah R M, El-Sehiemy R A, Wang G G. A novel parallel hurricane optimization algorithm for secure emission/economic load dispatch solution. Applied Soft Computing, 2018, 63, 206–222.

    Article  Google Scholar 

  9. Yi J H, Deb S, Dong J, Alavi A H, Wang G. An improved NSGA-III Algorithm with adaptive mutation operator for big data optimization problems. Future Generation Computer Systems, 2018, 88, 571–585.

    Article  Google Scholar 

  10. Wang G G, Cai X, Cui Z, Min G, Chen J. High performance computing for cyber physical social systems by using evolutionary multi-objective optimization algorithm. IEEE Transactions on Emerging Topics in Computing, 2017, DOI: https://doi.org/10.1109/TETC.2017.2703784.

  11. Wang G G, Tan Y. Improving metaheuristic algorithms with information feedback models. IEEE transactions on cybernetics, 2017, 49, 542–555.

    Article  Google Scholar 

  12. Yang X S, Deb S. Cuckoo search via Lévy flights. Proceedings of the 2009 World Congress on Nature & Biologically Inspired Computing, Coimbatore, India, 2009.

  13. Viswanathan G M, Afanasyev V, Buldyrev S V. Revisiting Lévy flight search patterns of wandering albatrosses. Nature, 1996, 381, 413.

    Article  Google Scholar 

  14. Yang X S, Deb S. Cuckoo search: Recent advances and applications. Neural Computing and Applications, 2014, 24, 169–174.

    Article  Google Scholar 

  15. Nawi N M, Khan A, Rehman M Z. A new optimized cuckoo search recurrent neural network (CSRNN) algorithm. Proceedings of the 8th International Conference on Robotic, Vision, Signal Processing & Power Applications, Singapore, 2014, 335–341.

  16. Wang H, Wang W J, Sun H, Li C H, Rahnamayan S, Liu Y. A modified cuckoo search algorithm for flow shop scheduling problem with blocking. Proceedings of 2015 IEEE Congress on Evolutionary Computation, Sendai, Japan, 2015, 456–163.

  17. Wang H, Wang W J, Sun H, Cui Z H, Rahnamayan S, Zeng S Y. A new cuckoo search algorithm with hybrid strategies for flow shop scheduling problems. Soft Computing, 2017, 21, 4297–4307.

    Article  Google Scholar 

  18. Naik M K, Panda R. A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition. Applied Soft Computing, 2016, 38, 661–675.

    Article  Google Scholar 

  19. Bhandari A K, Singh V K, Kumar A. Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy. Expert Systems with Applications, 2014, 41, 3538–3560.

    Article  Google Scholar 

  20. Ji Y, Tang D, Blythe P. Short-term forecasting of available parking space using wavelet neural network model. IET Intelligent Transport Systems, 2014, 9, 202–209.

    Article  Google Scholar 

  21. Ji Y J, Chen X S, Wang W, Hu B. Short-term forecasting of parking space using particle swarm optimization-wavelet neural network model. Journal of Jilin University: Engineering and Technology Edition, 2014, 46, 399–405. (in Chinese)

    Google Scholar 

  22. Chen H P, Tu X H, Wang Y, Wang Y. Short-term parking space prediction based on wavelet ELM neural networks. Journal of Jilin University: Science Edition, 2017, 2 388–392. (in Chinese)

    Google Scholar 

  23. Han Y, Zheng Z, Zhao J, Liu C Y. Forecasting of effective parking space based on grey-distributed wavelet neural network model. Journal of Transportation Systems Engineering and Information Technology, 2017, 2, 388–392. (in Chinese)

    Google Scholar 

  24. Chu P Z, Yang J S, Zhang L, Gao C, Sun J. Service data analyze for the available parking spaces in different car parks and their forecast problem. Proceedings of the 2017 International Conference on Management Engineering, Software Engineering and Service Sciences, Wu Han, China, 2017, 85–89.

  25. Deb K, Pratap A, Agarwal S, Sameer A, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 2002, 6, 182–197.

    Article  Google Scholar 

  26. Zitzler E, Laumanns M, Thiele L. SPEA2: Improving the strength Pareto evolutionary algorithm. TIK-report, 2001, 103.

  27. Nepomuceno F V, Engelbrecht A P. A self-adaptive heterogeneous PSO for real-parameter optimization. Proceedings of the 2013 IEEE Congress on Evolutionary Computation, Cancun, Mexico, 2013, 361–368.

  28. Coelho L D, Ayala H V H, Freire R Z. Population’s variance-based adaptive differential evolution for real parameter optimization. Proceedings of 2013 IEEE Congress on Evolutionary Computation, Cancun, Mexico, 2013, 1672–1677.

Download references

Acknowledgment

This study is supported in part by the National Key Research and Development Program of China (No. 2018YFC0831706), the National Natural Science Foundation of China (No. 61876070), in part by the National Natural Science Foundation of China (No. 61672259), in part by the National Natural Science Foundation of China (No. 61602203), and in part by the Natural Science Foundation of Jilin Province (No. 20170520064JH).

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Rui Guo, Xuanjing Shen & Hui Kang

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China

    Rui Guo, Xuanjing Shen & Hui Kang

Authors
  1. Rui Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xuanjing Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hui Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hui Kang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, R., Shen, X. & Kang, H. Improved CS Algorithm and its Application in Parking Space Prediction. J Bionic Eng 17, 1075–1083 (2020). https://doi.org/10.1007/s42235-020-0056-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42235-020-0056-x

Keywords

Search

Navigation