New intelligent particle swarm optimization algorithm with extreme learning machine for forecasting Pattavia pineapple productivity: case study of Loei and Nong Khai provinces in Thailand

References

• Alameer, Z., Elaziz, M. A., Ewees, A. A., Ye, H., & Jianhua, Z. (2019). Forecasting copper prices using hybrid adaptive neuro-fuzzy inference system and genetic algorithms. Natural Resources Research, 28(4), 1385–1401. https://doi.org/10.1007/s11053-019-09473-w
   Web of Science ®Google Scholar
• Babri, H.A., Huang, G. B., & Chen, Y. Q. (2000). Classification ability of single hidden layer feedforward neural networksIEEE Transactions on Neural Networks, 11(3), 799–801. https://doi.org/10.1109/72.846750
   PubMedGoogle Scholar
• Bradshaw, J. E. (2016). Mutation breeding. Plant breeding: Past, present and future. Springer.
   Google Scholar
• Bui, X. N., Nguyen, H., Tran, Q. H., Nguyen, D. A., & Bui, H. B. (2021). Predicting ground vibrations due to mine blasting using a novel artificial neural network-based cuckoo search optimization. Natural Resources Research, 30(3), 2663–2685. https://doi.org/10.1007/s11053-021-09823-7
   Web of Science ®Google Scholar
• Chu, Z., Ma, Y., & Cui, J. (2018). Adaptive reactionless control strategy via the PSO-ELM algorithm for free-floating space robots during manipulation of unknown objects. Nonlinear Dynamics, 91(2), 1321–1335. https://doi.org/10.1007/s11071-017-3947-6
   Web of Science ®Google Scholar
• Cordon, O., Herrera, F., & Stützle, T. (2002). A review on the ant colony optimization metaheuristic: Basis, models and new trends. Mathware and Soft Computing, 9(2-3), 141–175. https://upcommons.upc.edu/bitstream/handle/2099/3624/1-cordon-herrera-stuetzle.pdf
   Google Scholar
• Dorigo, M., & Di Caro, G. (1999). Ant colony optimization: A new meta-heuristic [Paper presentation]. Proceedings of the Congress on Evolutionary Computation CEC 1999 (Cat. No. 99TH8406), Washington, DC, USA, pp. 1470–1477. Vol. 2. https://doi.org/10.1109/CEC.1999.782657
   Google Scholar
• Dorigo, M., Dorigo., & M., Birattari. (2010). Ant colony optimization. Encyclopedia of machine learning (pp. 36–39). Springer.
   Google Scholar
• Eabsirimatee, P., Suthikarnnarunai, N., & Janpong, S. (2016). Thailand’s canned pineapple forecasting using multiple regression model and artificial neural network. Journal of Nakhonratchasima College, 10(2), 9–21. http://journal.nmc.ac.th/th/admin/Journal/2559Vol10No2_2.pdf
   Google Scholar
• Ertugrul, O. F. (2016). Forecasting electricity load by a novel recurrent extreme learning machines approach. International Journal of Electrical Power & Energy Systems. 78, 429–435. https://doi.org/10.1016/j.ijepes.2015.12.006
   Web of Science ®Google Scholar
• Figueiredo, E. M., & Ludermir, T. B. (2014). Investigating the use of alternative topologies on performance of the PSO-ELM. Neurocomputing, 127, 4–12. https://doi.org/10.1016/j.neucom.2013.05.047
   Web of Science ®Google Scholar
• Gandomi, A. H., Yang, X. S., & Alavi, A. H. (2013). Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems. Engineering with Computers, 29(1), 17–35. https://doi.org/10.1007/s00366-011-0241-y
   Web of Science ®Google Scholar
• Han, F., & Liu, Q. (2014). A diversity-guided hybrid particle swarm optimization based on gradient search. Neurocomputing, 137, 234–240. https://doi.org/10.1016/j.neucom.2013.03.074
   Web of Science ®Google Scholar
• Han, F., & Liu, Q. (2015). An improved hybrid PSO based on ARPSO and the quasi-newton method. Advances in swarm and computational intelligence (pp. 460–467). Springer International Publishing.
   Google Scholar
• Han, F., & Zhu, J. (2011). An improved ARPSO for feedforward neural networks. Proceedings of the Seventh International Conference Natural Computation (Vol. 2, pp. 1146–1150). Shanghai, China. https://doi.org/10.1109/ICNC.2011.6022153
   Google Scholar
• Huang, G. B., Wang, D. H., & Lan, Y. (2011). Extreme learning machines: A survey. International Journal of Machine Learning and Cybernetics, 2(2), 107–122. https://doi.org/10.1007/s13042-011-0019-y
   Web of Science ®Google Scholar
• Huang, G. B., Zhou, H., Ding, X., & Zhang, R. (2012). Extreme learning machine for regression and multiclass classification. In IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) (Vol. 42, pp. 513–529). https://doi.org/10.1109/TSMCB.2011.2168604
   PubMed Web of Science ®Google Scholar
• Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2004). Extreme learning machine: A new learning scheme of feedforward neural networks. 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541) (Vol. 2, pp. 985–990). Budapest, Hungary, 2004, https://doi.org/10.1109/IJCNN.2004.1380068
   Google Scholar
• Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70(1–3), 489–501. https://doi.org/10.1016/j.neucom.2005.12.126
   Web of Science ®Google Scholar
• Karaboga, D., & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing. 8(1), 687–697. https://doi.org/10.1016/j.asoc.2007.05.007
   Web of Science ®Google Scholar
• Ke, B., Nguyen, H., Bui, X. N., Bui, H. B., Choi, Y., Zhou, J., Moayedi, H., Costache, R., & Nguyen-Trang, T. (2021). Predicting the sorption efficiency of heavy metal based on the biochar characteristics, metal sources, and environmental conditions using various novel hybrid machine learning models. Chemosphere, 276, 130204. https://doi.org/10.1016/j.chemosphere.2021.130204
   PubMed Web of Science ®Google Scholar
• Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of the ICNN’95–International Conference on Neural Networks (Vol. 4, pp. 1942–1948). 27 November–1 December. Perth, WA, Australia. https://doi.org/10.1109/ICNN.1995.488968
   Google Scholar
• Kochenderfer, M. J., & Wheeler, T. A. (2019). Algorithms for optimization. Mit Press.
   Google Scholar
• Kumar, S., Solanki, V. K., Choudhary, S. K., Selamat, A., & Crespo, R. G. (2020). Comparative study on ant colony optimization (ACO) and K-means clustering approaches for jobs scheduling and energy optimization model in internet of things (IoT). International Journal of Interactive Multimedia and Artificial Intelligence, 6(1), 107. https://doi.org/10.9781/ijimai.2020.01.003
   Web of Science ®Google Scholar
• Lalwani, S., Sharma, H., Satapathy, S. C., Deep, K., & Bansal, J. C. (2019). A survey on parallel particle swarm optimization algorithms. Arabian Journal for Science and Engineering, 44(4), 2899–2923. https://doi.org/10.1007/s13369-018-03713-6
   Web of Science ®Google Scholar
• Li, L., Wang, W., & Xu, X. (2017). Multi-objective particle swarm optimization based on global margin ranking. I Nformation Sciences, 375, 30–47. https://doi.org/10.1016/j.ins.2016.08.043
   Web of Science ®Google Scholar
• Li, Y., Zhan, Z.-H., Lin, S., Zhang, J., & Luo, X. (2015). Competitive and cooperative particle swarm optimization with information sharing mechanism for global optimization problems. Information Sciences, 293, 370–382. https://doi.org/10.1016/j.ins.2014.09.030
   Web of Science ®Google Scholar
• Livingstone, D. J. (2008). Artificial neural networks: Methods and applications. Springer.
   Google Scholar
• Luan, J., Yao, Z., Zhao, F., & Song, X. (2019). A novel method to solve supplier selection problem: Hybrid algorithm of genetic algorithm and ant colony optimization. Mathematics and Computers in Simulation, 156, 294–309. https://doi.org/10.1016/j.matcom.2018.08.011
   Web of Science ®Google Scholar
• Lv, Z., Wang, L., Han, Z., Zhao, J., & Wang, W. (2019). Surrogate-assisted particle swarm optimization algorithm with pareto active learning for expensive multi-objective optimization. IEEE/CAA Journal of Automatica Sinica, 6(3), 838–849. https://doi.org/10.1109/JAS.2019.1911450
   Google Scholar
• Mayuree, S., & Wullapa, W. (2014). Prediction model for crude oil price using artificial neural networks. Applied Mathematical Sciences, 8(80), 3953–3965. http://dx.doi.org/10.12988/ams.2014.43193
   Google Scholar
• Mullen, R. J., Monekosso, D., Barman, S., & Remagnino, P. (2009). A review of ant algorithms. Expert Systems with Applications. 36(6), 9608–9617. https://doi.org/10.1016/j.eswa.2009.01.020
   Web of Science ®Google Scholar
• Nguyen, H., Bui, X. N., Bui, H. B., & Mai, N. L. (2018). A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine Vietnam. Neural Computing and Applications, 32(8), 3939–3955. https://doi.org/10.1007/s00521-018-3717-5
   Web of Science ®Google Scholar
• Nguyen, H., Drebenstedt, C., Bui, X.-N., & Bui, D. T. (2019). Prediction of blast-induced ground vibration in an open-pit mine by a novel hybrid model based on clustering and artificial neural network. Natural Resources Research, 29(2), 691–709. https://doi.org/10.1007/s11053-019-09470-z
   Web of Science ®Google Scholar
• Pahuja, G., & Nagabhushan, T. (2016). A novel GA-ELM approach for parkinson’s disease detection using brain structural T1-weighted MRI data [Paper presentation]. 2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) (pp. 1–6). Mysuru, India. https://doi.org/10.1109/CCIP.2016.7802848
   Google Scholar
• Paniri, M., Dowlatshahi, M. B., & Nezamabadi-Pour, H. (2020). MLACO: A multi-label feature selection algorithm based on ant colony optimization. Knowledge-Based Systems, 192, 105285. https://doi.org/10.1016/j.knosys.2019.105285
   Web of Science ®Google Scholar
• Pare, S., Kumar, A., Bajaj, V., & Singh, G. K. (2019). A context sensitive multilevel thresholding using swarm based algorithms. IEEE/CAA Journal of Automatica Sinica, 6, 1471–1486. https://doi.org/10.1109/JAS.2017.7510697
   Google Scholar
• Raslan, A. F., Ali, A. F., & Darwish, A. (2020). Swarm intelligence algorithms and their applications in internet of things. Swarm intelligence for resource management in internet of things; intelligent data-centric systems (pp. 1–19). Academic Press
   Google Scholar
• Sattar, A. M., Ertuğrul, Ö. F., Gharabaghi, B., McBean, E. A., & Cao, J. (2019). Extreme learning machine model for water network management. Neural Computing and Applications, 31(1), 157–169. https://doi.org/10.1007/s00521-017-2987-7
   Web of Science ®Google Scholar
• Singh, G., Kumar, N., & Kumar Verma, A. (2012). Ant colony algorithms in MANETs: A review. Journal of Network and Computer Applications, 35(6), 1964–1972. https://doi.org/10.1016/j.jnca.2012.07.018
   Web of Science ®Google Scholar
• Sweetlin, J. D., Nehemiah, H. K., & Kannan, A. (2017). Feature selection using ant colony optimization with tandem-run recruitment to diagnose bronchitis from CT scan images. Computer Methods and Programs in Biomedicine, 145, 115–125. https://doi.org/10.1016/j.cmpb.2017.04.009
   PubMed Web of Science ®Google Scholar
• Tran, B., Xue, B., Zhang, M., & Nguyen, S. (2016). Investigation on particle swarm optimisation for feature selection on high-dimensional data: Local search and selection bias. Connection Science, 28(3), 270–294. https://doi.org/10.1080/09540091.2016.1185392
   Web of Science ®Google Scholar
• Van Heeswijk, M., Miche, Y., Lindh-Knuutila, T., Hilbers, P. A., Honkela, T., Oja, E., & Lendasse, A. (2009). Adaptive ensemble models of extreme learning machines for time series prediction. International Conference on Artificial Neural Networks (pp. 305–314). Springer.
   Google Scholar
• Wang, D., Luo, H., Grunder, O., Lin, Y., & Guo, H. (2017). Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm. Applied Energy, 190, 390–407. https://doi.org/10.1016/j.apenergy.2016.12.134
   Web of Science ®Google Scholar
• Wang, F., Zhang, H., Li, K., Lin, Z., Yang, J., & Shen, X. L. (2018). A hybrid particle swarm optimization algorithm using adaptive learning strategy. Information Sciences, 436–437, 162–177. https://doi.org/10.1016/j.ins.2018.01.027
   Web of Science ®Google Scholar
• Wang, X., Wang, C., & Li, Q. (2017). Short-term wind power prediction using GA-ELM. The Open Electrical & Electronic Engineering Journal, 11, 48–56.
   Google Scholar
• Wullapa, W., & Suntaree, B. (2020). Criminal cases forecasting model using A new intelligent hybrid artificial neural network with cuckoo search algorithm. IAENG International Journal of Computer Science, 47, 3.
   Google Scholar
• Yan, X., Liu, Y., Xu, Y., & Jia, M. (2020). Multistep forecasting for diurnal wind speed based on hybrid deep learning model with improved singular spectrum decomposition. Energy Conversion and Management, 225, 113456. https://doi.org/10.1016/j.enconman.2020.113456
   Web of Science ®Google Scholar
• Yang, X. S. (2009). Firefly algorithms for multimodal optimization. In O. Watanabe, & T. Zeugmann (Eds.), Stochastic algorithms: Foundations and applications (pp. 169–178). Springer.
   Google Scholar
• Yaseen, Z. M., Sulaiman, S. O., Deo, R. C., & Chau, K. W. (2019). An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction. Journal of Hydrology, 569, 387–408. https://doi.org/10.1016/j.jhydrol.2018.11.069
   Web of Science ®Google Scholar
• Ye, Y., & Qin, Y. (2015). QR factorization based incremental extreme learning machine with growth of hidden nodes. Pattern Recognition Letters, 65, 177–183. https://doi.org/10.1016/j.patrec.2015.07.031
   Web of Science ®Google Scholar
• Yu, L., Liang, S., Chen, R., & Lai, K. K. (2022). Predicting monthly biofuel production using a hybrid ensemble forecasting methodology. International Journal of Forecasting, 38(1), 3–20. https://doi.org/10.1016/j.ijforecast.2019.08.014
   Web of Science ®Google Scholar
• Yu, Z., Shi, X., Miao, X., Zhou, J., Khandelwal, M., Chen, X., & Qiu, Y. (2021). Intelligent modeling of blast-induced rock movement prediction using dimensional analysis and optimized artificial neural network technique. International Journal of Rock Mechanics and Mining Sciences. 143, 104794. https://doi.org/10.1016/j.ijrmms.2021.104794
   Web of Science ®Google Scholar
• Zhang, H., Nguyen, H., Bui, X. N., Nguyen-Thoi, T., Bui, T. T., Nguyen, N., Vu, D. A., Mahesh, V., & Moayedi, H. (2020). Developing a novel artificial intelligence model to estimate the capital cost of mining projects using deep neural network-based ant colony optimization algorithm. Resources Policy, 66, 101604. https://doi.org/10.1016/j.resourpol.2020.101604
   Web of Science ®Google Scholar
• Zhang, J., Meng, Y., Wei, J., Chen, J., & Qin, J. (2021). A novel hybrid deep learning model for sugar price orecasting based on time series decomposition. Mathematical Problems in Engineering, 2021, 1–9. https://doi.org/10.1155/2021/6507688
   Web of Science ®Google Scholar
• Zhang, X., Nguyen, H., Bui, X. N., Le, H. A., Nguyen-Thoi, T., Moayedi, H., & Mahesh, V. (2020). Evaluating and predicting the stability of roadways in tunnelling and underground space using artificial neural network-based particle swarm optimization. Tunnelling and Underground Space Technology, 103, 103517. https://doi.org/10.1016/j.tust.2020.103517
   Web of Science ®Google Scholar
• Zhang, X., Nguyen, H., Bui, X. N., Tran, Q. H., Nguyen, D. A., Bui, D. T., & Moayedi, H. (2019). Novel soft computing model for predicting blast-induced ground vibration in open-pit mines based on particle swarm optimization and XGBoost. Natural Resources Research, 29(2), 711–721. https://doi.org/10.1007/s11053-019-09492-7
   Web of Science ®Google Scholar