THAILAND PORT THROUGHPUT PREDICTION VIA PARTICLE SWARM OPTIMIZATION BASED NEURAL NETWORK

DOI: 10.5937/jaes18-25687

This is an open access article distributed under the CC BY 4.0

Volume 18 article 698 pages: 338 - 345

Shipping volume in Thailand have signifi cantly increased in last four years. It is important to pay attention to the trend
of Thailand port throughput and use as the guideline to prepare for the needs of supporting facilities, infrastructures,
fi nancial and human resources. An effective forecasting technique called particle swarm based neural network (PSONN)
is developed to estimate Thailand port throughput in this work. The prediction results from PSONN and classical
backpropagation training algorithm, backpropagation neural networks (BPNN) were compared. The results shown
that PSONN provides more accurate results than BPNN when apply to predict port throughput of Thailand. The
mean squared error obtained from PSONN are about 10 times lower than that of BPNN. This confi rms that neural
network based on PSO training algorithm has better performance and better ability to escape local optimum than that
of BPNN.

1. Dokeroglu, T., Sevinc, E., Kucukyilmaz, T., & Cosar, A. (2019). A survey on new generation metaheuristic algorithms. Computers & Industrial Engineering, 137, 106040.

2. Hassan, R., Cohanim, B., De Weck, O., & Venter, G. (2005, April). A comparison of particle swarm optimization and the genetic algorithm. In 46th AIAA/ASME/ ASCE/AHS/ASC structures, structural dynamics and materials conference (p. 1897).

3. Hamed, H. N. A., & Haza, N. (2006). Particle swarm optimization for neural network learning enhancement (Doctoral dissertation, Universiti Teknologi Malaysia).

4. UNCTAD, PORT MANAGEMENT SERIES 2016. Port Performance Linking Performance Indicators to Strategic Objectives, 4, pp. 11-12.

5. Sorrosal, G., Irigoyen, E., Borges, C. E., Martin, C., Macarulla, A. M., & Alonso-Vicario, A. (2017). Artifi cial neural network modelling of the bioethanol- to-olefi ns process on a HZSM-5 catalyst treated with alkali. Applied Soft Computing, 58, 648-656.

6. Kwon, H. B. (2017). Exploring the predictive potential of artifi cial neural networks in conjunction with DEA in railroad performance modeling. International Journal of Production Economics, 183, 159-170.

7. Witoonchart, P., & Chongstitvatana, P. (2017). Application of structured support vector machine backpropagation to a convolutional neural network for human pose estimation. Neural Networks, 92, 39-46.

8. Kwon, H. B., & Lee, J. (2015). Two-stage production modeling of large US banks: A DEA-neural network approach. Expert Systems with Applications, 42(19), 6758-6766.

9. Gordini, N., & Veglio, V. (2017). Customers churn prediction and marketing retention strategies. An application of support vector machines based on the AUC parameter-selection technique in B2B e-commerce industry. Industrial Marketing Management, 62, 100-107.

10. Chen, F. H., Chi, D. J., & Wang, Y. C. (2015). Detecting biotechnology industry's earnings management using Bayesian network, principal component analysis, back propagation neural network, and decision tree. Economic Modelling, 46, 1-10.

11. Kordanuli, B., Barjaktarovic, L., Jeremic, L., & Alizamir, M. (2017). Appraisal of artifi cial neural network for forecasting of economic parameters. Physica A: Statistical Mechanics and its Applications, 465, 515-519.

12. Samanta, B., & Nataraj, C. (2008). Automated diagnosis of cardiac state in healthcare systems using computational intelligence. International Journal of Services Operations and Informatics, 3(2), 162-177.

13. Shaikhina, T., & Khovanova, N. A. (2017). Handling limited datasets with neural networks in medical applications: A small-data approach. Artifi cial intelligence in medicine, 75, 51-63.

14. Phyo, P. P., & Jeenanunta, C. (2019). Electricity load forecasting using a deep neural network. Engineering and Applied Science Research, 46(1), 10-17.

15. Ma, L., Hu, S., Qiu, M., Li, Q., & Ji, Z. (2017). Energy consumption optimization of high sulfur natural gas purifi cation plant based on back propagation neural network and genetic algorithms. Energy Procedia, 105, 5166-5171.

16. Rumelhart, D. E. (1986). Parallel distributed processing: Explorations in the microstructure of cognition. Learning internal representations by error propagation, 1, 318-362.

17. Schaffer, J. D., Whitley, D., & Eshelman, L. J. (1992, June). Combinations of genetic algorithms and neural networks: A survey of the state of the art. In [Proceedings] COGANN-92: International Workshop on Combinations of Genetic Algorithms and Neural Networks (pp. 1-37). IEEE.

18. Socha, K., & Blum, C. (2007). An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training. Neural Computing and Applications, 16(3), 235-247.

19. Mazurowski, M. A., Habas, P. A., Zurada, J. M., Lo, J. Y., Baker, J. A., & Tourassi, G. D. (2008). Training neural network classifi ers for medical decision making: The effects of imbalanced datasets on classifi - cation performance. Neural networks, 21(2-3), 427- 436.

20. Valian, E., Mohanna, S., &Tavakoli, S. (2011). Improved cuckoo search algorithm for feedforward neural network training. International Journal of Artifi cial Intelligence & Applications, 2(3), 36-43.

21. Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN' 95-International Conference on Neural Networks (Vol. 4, pp. 1942-1948). IEEE.

22. Cheng, Z., & Juncheng, T. (2015). Adaptive combination forecasting model for China’s logistics freight volume based on an improved PSO-BP neural network. Kybernetes.

23. Liu, H., Zhang, Q., & Wang, W. (2011). Research on location - routing problem of reverse logistics with grey recycling demands based on PSO. Grey Systems: Theory and Application.

24. Salmeron, J. L., Rahimi, S. A., Navali, A. M., & Sadeghpour, A. (2017). Medical diagnosis of Rheumatoid Arthritis using data driven PSO–FCM with scarce datasets. Neurocomputing, 232, 104-112.

25. Kim, H. H., Choi, J. Y., & Park, S. C. (2017). Tire mixing process scheduling using particle swarm optimization. Computers & Industrial Engineering, 110, 333-343.

26. Sangsawang, C., & Sethanan, K. (2016). Hybrid particle swarm optimization with a Cauchy distribution for solving a reentrant fl exible fl ow shop problem with a blocking constraint. Engineering and Applied Science Research, 43(2), 55-61.

27. Gudise, V. G., & Venayagamoorthy, G. K. (2003, April). Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks. In Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No. 03EX706) (pp. 110-117). IEEE.

28. Che, Z. H. (2010). PSO-based back-propagation artifi cial neural network for product and mold cost estimation of plastic injection molding. Computers & Industrial Engineering, 58(4), 625-637.

29. Wang, H. S., Wang, Y. N., & Wang, Y. C. (2013). Cost estimation of plastic injection molding parts through integration of PSO and BP neural network. Expert Systems with Applications, 40(2), 418-428.

30. Pwasong, A., &Sathasivam, S. (2016). A new hybrid quadratic regression and cascade forward backpropagation neural network. Neurocomputing, 182, 197-209.

31. Gordan, B., Armaghani, D. J., Hajihassani, M., & Monjezi, M. (2016). Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Engineering with Computers, 32(1), 85-97.

32. Ibrahim, A. M., & El-Amary, N. H. (2018). Particle Swarm Optimization trained recurrent neural network for voltage instability prediction. Journal of Electrical Systems and Information Technology, 5(2), 216-228.

33. Engelbrecht, A. P. (2007). Computational intelligence:
an introduction. John Wiley & Sons.