Document Type : Original Article
Authors
- Lin Yongxing ^{} ^{1}
- Si Yanru ^{} ^{2}
^{1} School of Computer and Communication, Lanzhou University of Technology, Gansu province, 730050, China
^{2} School of Finance, Taxation and Public Administration, Lanzhou University of Finance and Economics, Gansu province, 730101, China
Abstract
One of the most widely used metals in the world is the Iron. The world cost of iron ore is defineded by its supply and demand. Numerous variabes such as steel, scrap, oil, gold, interest rate, inflation rate, dollar value, and stock value affect the world price of iron ore. Therefore, for economic investment of iron ore, it should be forecasted precisely by the scientists to give a direction to the decision makers to make a proper decision for the society. Due to the multiplicity of effective parameters and the complexity of the relationships between the iron ore variables, artificial intelligence is the best idea for forecasting. In this paper, we utilized a new optimized version of Convolutional Neural Network (CNN) to facilitate this task. To do so, a modified version of the Search and Rescue (MSAR) optimization algorithm has been designed and used for optimizing the CNN for improving its training efficiency in forecasting the iron ore price volatilities. The method is then validated based on ten different variables. Finally, a comparison of the results with various state of the art techniques was carried out to show the suggested method effectiveness. The results showed that the suggested technique has the fittest results in comparison to the other newest techniques.
Keywords
2. Ghiasi, M., N. Ghadimi, and E. Ahmadinia, An analytical methodology for reliability assessment and failure analysis in distributed power system. SN Applied Sciences, 2019. 1(1): p. 44.
3. Ramezani, M., D. Bahmanyar, A new optimal energy management strategy based on improved multi-objective antlion optimization algorithm: applications in smart home. SN Applied Sciences, 2020. 2(12): p. 1-17.
4. Mir, M., et al., Application of hybrid forecast engine based intelligent algorithm and feature selection for wind signal prediction. Evolving Systems, 2020. 11(4): p. 559-573.
5. Firouz, M.H. and N. Ghadimi, Concordant controllers based on FACTS and FPSS for solving wide-area in multi-machine power system. Journal of Intelligent & Fuzzy Systems, 2016. 30(2): p. 845-859.
6. Mir, M., et al., Employing a Gaussian Particle Swarm Optimization method for tuning Multi Input Multi Output‐fuzzy system as an integrated controller of a micro‐grid with stability analysis. Computational Intelligence, 2020. 36(1): p. 225-258.
7. Liu, Y., W. Wang, and N. Ghadimi, Electricity load forecasting by an improved forecast engine for building level consumers. Energy, 2017. 139: p. 18-30.
8. Yang, Z., et al., Model Parameter Estimation of the PEMFCs Using Improved Barnacles Mating Optimization Algorithm. Energy, 2020: p. 118738.
9. Yu, D., et al., Energy management of wind-PV-storage-grid based large electricity consumer using robust optimization technique. Journal of Energy Storage, 2020. 27: p. 101054.
10. Akbary, P., et al., Extracting appropriate nodal marginal prices for all types of committed reserve. Computational Economics, 2019. 53(1): p. 1-26.
11. Hosseini-Firouz, M. and N. Ghadimi, Financial planning for the preventive maintenance of the power distribution systems critical components using the reliability-centered approach. International Journal of Physical Sciences, 2015. 10(3): p. 123-132.
12. Gollou, A.R. and N. Ghadimi, A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets. Journal of Intelligent & Fuzzy Systems, 2017. 32(6): p. 4031-4045.
13. Ye, H., et al., High step-up interleaved dc/dc converter with high efficiency. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2020: p. 1-20.
14. Fan, X., et al., High voltage gain DC/DC converter using coupled inductor and VM techniques. IEEE Access, 2020. 8: p. 131975-131987.
15. Hamian, M., et al., A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on mixed integer genetic algorithm. Engineering Applications of Artificial Intelligence, 2018. 72: p. 203-212.
16. Khodaei, H., et al., Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer using compromise programming. Applied Thermal Engineering, 2018. 137: p. 395-405.
17. Wang, Z.-X., Y.-F. Zhao, and L.-Y. He, Forecasting the monthly iron ore import of China using a model combining empirical mode decomposition, non-linear autoregressive neural network, and autoregressive integrated moving average. Applied Soft Computing, 2020: p. 106475.
18. Gong, W. and N. razmjooy, A new optimisation algorithm based on OCM and PCM solution through energy reserve. International Journal of Ambient Energy, 2020: p. 1-14.
19. Ramos, A.L., et al., Evaluation of an iron ore price forecast using a geometric Brownian motion model. REM - International Engineering Journal, 2019. 72: p. 9-15.
20. Lee, W.C., et al., Forecasting of Iron Ore Prices using Machine Learning. Journal of the Korea Industrial Information Systems Research, 2020. 25(2): p. 57-72.
21. Li, D., et al., Development of a Group Method of Data Handling Technique to Forecast Iron Ore Price. Applied Sciences, 2020. 10(7): p. 2364.
22. Ewees, A.A., et al., Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility. Resources Policy, 2020. 65: p. 101555.
23. Yu, D., et al., System identification of PEM fuel cells using an improved Elman neural network and a new hybrid optimization algorithm. Energy Reports, 2019. 5: p. 1365-1374.
24. Razmjooy, N., V.V. Estrela, and H.J. Loschi, A study on metaheuristic-based neural networks for image segmentation purposes, in Data Science. 2019, CRC Press. p. 25-49.
25. Schmidhuber, J., Deep learning in neural networks: An overview. Neural networks, 2015. 61: p. 85-117.
26. Acharya, U.R., et al., Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Computers in biology and medicine, 2018. 100: p. 270-278.
27. Koehler, F. and A. Risteski, Representational Power of ReLU Networks and Polynomial Kernels: Beyond Worst-Case Analysis. arXiv preprint arXiv:1805.11405, 2018.
28. Ward, R., X. Wu, and L. Bottou. AdaGrad stepsizes: Sharp convergence over nonconvex landscapes. in International Conference on Machine Learning. 2019. PMLR.
29. Roy, K., K.K. Mandal, and A.C. Mandal, Ant-Lion Optimizer algorithm and recurrent neural network for energy management of micro grid connected system. Energy, 2019. 167: p. 402-416.
30. Razmjooy, N., F.R. Sheykhahmad, and N. Ghadimi, A hybrid neural network–world cup optimization algorithm for melanoma detection. Open Medicine, 2018. 13(1): p. 9-16.
31. Van Merriënboer, B., et al., Blocks and fuel: Frameworks for deep learning. arXiv preprint arXiv:1506.00619, 2015.
32. Martens, J. and I. Sutskever. Learning recurrent neural networks with hessian-free optimization. in Proceedings of the 28th International Conference on Machine Learning (ICML-11). 2011. Citeseer.
33. Bengio, Y., et al. Greedy layer-wise training of deep networks. in Advances in neural information processing systems. 2007.
34. Zhang, G., et al., Optimal parameter extraction of PEM fuel cells by meta-heuristics. International Journal of Ambient Energy, 2020: p. 1-10.
35. Yin, Z. and N. Razmjooy, PEMFC identification using deep learning developed by improved deer hunting optimization algorithm. International Journal of Power and Energy Systems, 2020. 40(2).
36. Yanda, L., et al., Optimal arrangement of a micro-CHP system in the presence of fuel cell-heat pump based on metaheuristics. International Journal of Ambient Energy, 2020: p. 1-12.
37. Guo, Y., et al., An optimal configuration for a battery and PEM fuel cell-based hybrid energy system using developed Krill herd optimization algorithm for locomotive application. Energy Reports, 2020. 6: p. 885-894.
38. Yuan, Z., et al., A new technique for optimal estimation of the circuit-based PEMFCs using developed Sunflower Optimization Algorithm. Energy Reports, 2020. 6: p. 662-671.
39. Cao, Y., et al., Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm. Energy Reports, 2019. 5: p. 1616-1625.
40. Fei, X., R. Xuejun, Optimal configuration and energy management for combined solar chimney, solid oxide electrolysis, and fuel cell: a case study in Iran. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2019: p. 1-21.
41. Bagheri, M., et al. Multi-objective Shark Smell Optimization for Solving the Reactive Power Dispatch Problem. in 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe). 2018. IEEE.
42. Navid, R., M. Khalilpour, and M. Ramezani, A new meta-heuristic optimization algorithm inspired by FIFA world cup competitions: theory and its application in PID designing for AVR system. Journal of Control, Automation and Electrical Systems, 2016. 27(4): p. 419-440.
43. Rusdi, N.A., et al., Reconstruction of Medical Images Using Artificial Bee Colony Algorithm. Mathematical Problems in Engineering, 2018. 2018.
44. Shabani, A., et al., Search and rescue optimization algorithm: A new optimization method for solving constrained engineering optimization problems. Expert Systems with Applications, 2020. 161: p. 113698.
45. Feng, Y.-H. and G.-G. Wang, Binary moth search algorithm for discounted {0-1} knapsack problem. IEEE Access, 2018. 6: p. 10708-10719.
46. Elaziz, M.A., et al., Task scheduling in cloud computing based on hybrid moth search algorithm and differential evolution. Knowledge-Based Systems, 2019. 169: p. 39-52.
47. Strumberger, I., et al., Moth search algorithm for drone placement problem. International Journal of Computers, 2018. 3.
48. Strumberger, I., et al. Wireless sensor network localization problem by hybridized moth search algorithm. in 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC). 2018. IEEE.
49. Higashi, N. and H. Iba. Particle swarm optimization with Gaussian mutation. in Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS’03 (Cat. No. 03EX706). 2003. IEEE.
50. Dhiman, G. and V. Kumar, Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 2017. 114: p. 48-70.
51. Mirjalili, S., S.M. Mirjalili, and A. Hatamlou, Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 2016. 27(2): p. 495-513.
52. Cuevas, E., F. Fausto, and A. González, The Locust Swarm Optimization Algorithm, in New Advancements in Swarm Algorithms: Operators and Applications. 2020, Springer. p. 139-159.
53. Zhao, W., Z. Zhang, and L. Wang, Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications. Engineering Applications of Artificial Intelligence, 2020. 87: p. 103300.
54. Iron Ore Monthly Price - Australian Dollar per Dry Metric Ton. 2020; Available from: https://www.indexmundi.com/commodities/?commodity=iron-ore&months=60¤cy=aud.
55. Burgess, K. and N. Rohde, Can Exchange Rates Forecast Commodity Prices? Recent Evidence using Australian Data. Economics Bulletin, 2013. 33(1): p. 511-518.
56. Liu, C., et al., Forecasting copper prices by decision tree learning. Resources Policy, 2017. 52: p. 427-434.
57. Exchange rates. 2020; Available from: https://fred.stlouisfed.org/tags/series?t=china%3Bexchange+rate%3Bmonthly.
58. China Inflation Rate. 2020; Available from: https://www.macrotrends.net/countries/CHN/china/inflation-rate-cpi#:~:text=China%20inflation%20rate%20for%202019,a%200.56%25%20increase%20from%202015.
59. Oil Price. 2020; Available from: https://www.eia.gov/.
60. World Bank slashes outlook for oil, metals as coronavirus crushes demand. 2020; Available from: https://www.reuters.com/article/us-global-oil-worldbank-idUSKCN2252EC.
61. Commudity Index Price 2020; Available from: https://www.indexmundi.com/commodities/.
62. Gold Price. 2020; Available from: https://goldprice.org/.
63. Scrap Prices, News and Analysis. 2020; Available from: https://www.steelorbis.com/steel-market/scrap.htm?gclid=Cj0KCQiAtqL-BRC0ARIsAF4K3WG7xNGnlmlsUxBox3VRhGOy5JjxxZF4cA6WjsVXnBny9DE88_17Ei4aAjDLEALw_wcB.
64. Ma, W., X. Zhu, and M. Wang, Forecasting iron ore import and consumption of China using grey model optimized by particle swarm optimization algorithm. Resources Policy, 2013. 38(4): p. 613-620.
65. Wu, M., et al., An intelligent integrated optimization system for the proportioning of iron ore in a sintering process. Journal of Process Control, 2014. 24(1): p. 182-202.
66. NIU, D.-x., et al., Optimization of Forecasting Method Based on Genetic Neural Network and its Application [J]. Journal of North China Electric Power University, 2001. 1: p. 1-5.
67. Martínez, F., et al., Dealing with seasonality by narrowing the training set in time series forecasting with kNN. Expert Systems with Applications, 2018. 103: p. 38-48.
68. Patel, A.K., S. Chatterjee, and A.K. Gorai, Development of a machine vision system using the support vector machine regression (SVR) algorithm for the online prediction of iron ore grades. Earth Science Informatics, 2019. 12(2): p. 197-210.
69. Dehghani, H. and D. Bogdanovic, Copper price estimation using bat algorithm. Resources Policy, 2018. 55: p. 55-61.
70. Ravi, V., D. Pradeepkumar, and K. Deb, Financial time series prediction using hybrids of chaos theory, multi-layer perceptron and multi-objective evolutionary algorithms. Swarm and Evolutionary Computation, 2017. 36: p. 136-149.
71. Zhou, J., et al., Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting. Engineering with Computers, 2019: p. 1-10.
72. Liu, Y., et al., Influence of different factors on prices of upstream, middle and downstream products in China’s whole steel industry chain: Based on Adaptive Neural Fuzzy Inference System. Resources Policy, 2019. 60: p. 134-142.
73. Weng, F., et al. Application of Regularized Extreme Learning Machine Based on BIC Criterion and Genetic Algorithm in Iron Ore Price Forecasting. in 2018 3rd International Conference on Modelling, Simulation and Applied Mathematics (MSAM 2018). 2018. Atlantis Press.