Improved CS Algorithm and its Application in Parking Space Prediction
Source abstract
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.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Smart Parking Systems Research · Video Surveillance and Tracking Methods · Autonomous Vehicle Technology and Safety
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (28)
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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, 853–871.10.1007/s00521-012-1304-8 ↗
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, 147–157.10.1016/j.neucom.2015.11.018 ↗
3. Wang G G, Gandomi A H, Alavi A H. Stud krill herd algorithm. Neurocomputing, 2014, 128, 363–370.10.1016/j.neucom.2013.08.031 ↗
4. Wang G G, Guo L, Gandomi A H, Hao G, Wang H. Chaotic krill herd algorithm. Information Sciences, 2014, 274, 17–34.10.1016/j.ins.2014.02.123 ↗
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, 286–299.10.1504/IJBIC.2016.079569 ↗
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, 2454–2462.10.1016/j.apm.2013.10.052 ↗
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, 177–198.10.1007/s12293-017-0241-6 ↗
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, 206–222.10.1016/j.asoc.2017.12.002 ↗
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, 571–585.10.1016/j.future.2018.06.008 ↗
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.2017.2703784.10.1109/TETC.2017.2703784 ↗
11. Wang G G, Tan Y. Improving metaheuristic algorithms with information feedback models. IEEE transactions on cybernetics, 2017, 49, 542–555.10.1109/TCYB.2017.2780274 ↗
12. Yang X S, Deb S. Cuckoo search via Lévy flights. Proceedings of the 2009 World Congress on Nature & Biologically Inspired Computing, Coimbatore, India, 2009.10.1109/NABIC.2009.5393690 ↗
13. Viswanathan G M, Afanasyev V, Buldyrev S V. Revisiting Lévy flight search patterns of wandering albatrosses. Nature, 1996, 381, 413.10.1038/381413a0 ↗
14. Yang X S, Deb S. Cuckoo search: Recent advances and applications. Neural Computing and Applications, 2014, 24, 169–174.10.1007/s00521-013-1367-1 ↗
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, 335–341.10.1007/978-981-4585-42-2_39 ↗
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, 456–163.10.1109/CEC.2015.7256925 ↗
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, 4297–4307.10.1007/s00500-016-2062-9 ↗
18. Naik M K, Panda R. A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition. Applied Soft Computing, 2016, 38, 661–675.10.1016/j.asoc.2015.10.039 ↗
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, 3538–3560.10.1016/j.eswa.2013.10.059 ↗
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, 202–209.10.1049/iet-its.2013.0184 ↗
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)
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)
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)
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, 182–197.10.1109/4235.996017 ↗
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, 361–368.10.1109/CEC.2013.6557592 ↗
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, 1672–1677.10.1109/CEC.2013.6557762 ↗