1. Umbarkar, A.J., M.S. Joshi and W.C. Hong, 2016. Comparative study of diversity based parallel dual population genetic algorithm for unconstrained function optimizations. Int. J. Bio. Inspired Comput., 10.1504/IJBIC.2016.078638.
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2. Li, M.W., W.C. Hong, J. Geng and J. Wang, 2016. Berth and quay crane coordinated scheduling using chaos cloud particle swarm optimization algorithm. Neural Comput. Appli., 10.1007/s00521-016-2226-7.
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3. Li, M.W., J. Geng, W.C. Hong and Z.Y. Chen, 2016. A novel approach based on the Gauss-vSVR with a new hybrid evolutionary algorithm and input vector decision method for port throughput forecasting. Neural Comput. Appli., 10.1007/s00521-016-2396-3.
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4. Fan, G., L.L. Peng, W.C. Hong and F. Sun, 2016. Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression. Neurocomput., 173: 958-970.
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5. Fan, G., L.L. Peng, M.L. Huang and W.C. Hong, 2016. Hybridizing DEMD and quantum PSO with SVR in electric load forecasting. Energ., 9: 221-221.
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6. Chen, Y.H., W.C. Hong, W. Shen and N.N. Huang, 2016. Electric load forecasting based on LSSVM with fuzzy time series and global harmony search algorithm. Energ., 9: 1-13.
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7. Geng, J., M.L. Huang, M.W. Li and W.C. Hong, 2015. Hybridization of seasonal chaotic cloud simulated annealing algorithm in a SVR-based load forecasting model. Neurocomputing, 151: 1362-1373.
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8. Fan, G.F., L.L. Peng, W.C. Hong and F. Sun, 2015. Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression. Neurocomputing, (In Press). 10.1016/j.neucom.2015.08.051.
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9. Dong, Y., X. Chen, C.C. Li, W.C. Hong and Y. Xu, 2015. Consistency issues of interval pairwise comparison matrices. Soft Comput., 19: 2321-2335.
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10. Chen, R., C.Y. Liang, W.C. Hong and D.X. Gu, 2015. Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm. Applied Soft Computi., 26: 435-443.
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11. Zheng, F., E. Zhang, Y. Xu and W.C. Hong, 2014. Competitive analysis for make-to-order scheduling with reliable lead time quotation. J. Combinatorial Optimization, 27: 182-198.
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12. Wei, S.Y., N.Y. Chu and W.C. Hong, 2014. A straightforward approach to estimate the abnormal return in Taiwan`s real estate market. Int. J. Applied Evolutionary Comput., 5: 34-43.
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13. Umbarkar, A.J., M.S. Joshi and W.C. Hong, 2014. Multithreaded parallel dual population genetic algorithm (MPDPGA) for unconstrained function optimizations on multi-core system. Applied Math. Comput., 243: 936-949.
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14. Pandi, V.R., B.K. Panigrahi, W.C. Hong and R. Sharma, 2014. A multiobjective bacterial foraging algorithm to solve the environmental economic dispatch problem. Energy Sources Part B: Econ. Plann. Policy, 9: 236-247.
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15. Pan, B., S.Y. Wei, X. Xu and W.C. Hong, 2014. The impact of defense investment on economic growth in the perspective of time series-A case study of China. Int. J. Applied Evolutionary Comput., 5: 44-58.
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16. Fan, G., L.L. Peng, W.C. Hong and F. Sun, 2014. Kinetics for reduction of high phosphorus iron ore based on the phase space reconstruction. J. Applied Math., Vol. 2014. 10.1155/2014/514851.
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17. Fan, G., L.L. Peng, H. Wang and W.C. Hong, 2014. Non-isothermal kinetics model for direct reduction of phosphorus iron ore by powdered coal. Mining Metallurgical Eng., 34: 75-83.
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18. Dubey, H.M., B.K. Panigrahi, M. Pandit, A. Agarwal and W.C. Hong, 2014. Invasive weed optimization for combined economic and emission dispatch problems. Int. J. Applied Evolutionary Comput., 5: 1-18.
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19. Li, M., W.C. Hong and H. Kang, 2013. Urban traffic flow forecasting using Gauss-SVR with cat mapping, cloud model and PSO hybrid algorithm. Neurocomputing, 44: 230-240.
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20. Li, M., H. Kang, P. Zhou and W.C. Hong, 2013. Hybrid optimization algorithm based on chaos, cloud and particle swarm optimization algorithm. J. Syst. Eng. Electron., 24: 324-334.
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21. Ju, F.Y. and W.C. Hong, 2013. Application of seasonal SVR with chaotic gravitational search algorithm in electricity forecasting. Applied Mathem. Modelling, 37: 9643-9651.
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22. Hong, W.C., Y. Dong, W.Y. Zhang, L.Y. Chen and B.K. Panigrahi, 2013. Cyclic electric load forecasting by seasonal SVR with chaotic genetic algorithm. Int. J. Electrical Power Energy Syst., 44: 604-614.
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23. Fan, G., S. Qing, H. Wang, W.C. Hong and H.J. Li, 2013. Support vector regression model based on empirical mode decomposition and auto regression for electric load forecasting. Energies, 6: 1887-1901.
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24. Fan, G., S. Qing, H. Wang and W.C. Hong, 2013. The kinetic model for direct smelting reduction process of Huimin iron ore. Mineral Process. Extractive Metallurgy, 122: 105-112. Mineral Process. Extractive Metallurgy, 122: 105-112.
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25. Dong, Y., W.C. Hong, Y. Xu and S. Yu, 2013. Numerical scales generated individually for analytic hierarchy process. Eur. J. Operational Res., 229: 654-662.
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26. Dong, Y., W.C. Hong and Y. Xu, 2013. Measuring consistency of linguistic preference relations: A 2-tuple linguistic approach. Soft Comput., 17: 2117-2130.
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27. Dong, Y., G. Zhang, W.C. Hong and S. Yu, 2013. Linguistic computation model based on 2-tuples and intervals. IEEE Trans. Fuzzy Syst., 21: 1006-1018.
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28. Chiang, H.W., 2013. Intelligent Energy Demand Forecasting. Springer London.UK.ISBN: 978-1-44714-967-5.
29. Zhang, W.Y., W.C. Hong, Y. Dong, G. Tsai, J.T. Sung and G. Fan, 2012. Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting. Energy, 45: 850-858.
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30. Wei, S.Y., J.J.W. Yang and W.C. Hong, 2012. Will the deduction of daily price limit induce volatility decrease? Taiwan evidence. Adv. Financial Planning Forecasting, 5: 49-68.
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31. Hong, W.C., 2012. Application of seasonal SVR with chaotic immune algorithm in traffic flow forecasting. Neural Comput. Applic., 21: 583-593.
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32. Fan, G., S. Qing, H. Wang, Z. Shi, W.C. Hong and L. Dai, 2012. Study on apparent kinetic prediction model of the smelting reduction based on the time series. Math. Problems Eng., Vol. 2012. 10.1155/2012/720849.
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33. Wei, S.Y., W.C. Hong and K. Wang, 2011. Firm size transmission effect and price-volume relationship analysis during financial tsunami periods. Int. J. Applied Evolutionary Comput., 2: 59-78.
34. Wei, S.Y., J.J.W. Yang, J.T. Chen and W.C. Hong, 2011. The volatility for pre and post global financial crisis: An application of computational finance. Int. J. Applied Evolutionary Comput., 2: 82-95.
35. Hong, W.C., Y.C. Dong, L.Y. Chen and S.Y. Wei, 2011. SVR with hybrid chaotic genetic algorithms for tourism demand forecasting. Applied Soft Comput., 11: 1881-1890.
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36. Hong, W.C., Y. Dong, F. Zheng and S.Y. Wei, 2011. Hybrid evolutionary algorithms in a SVR traffic flow forecasting model. Applied Math. Comput., 217: 6733-6747.
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37. Hong, W.C., Y. Dong, F. Zheng and C.Y. Lai, 2011. Forecasting urban traffic flow by SVR with continuous ACO. Applied Math. Mod., 35: 1282-1291.
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38. Hong, W.C., Y. Dong, C.Y. Lai, L.Y. Chen and S.Y. Wei, 2011. SVR with hybrid chaotic immune algorithm for seasonal load demand forecasting. Energies, 4: 960-977.
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39. Hong, W.C., 2011. Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm. Neurocomputing, 74: 2096-2107.
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40. Hong, W.C., 2011. Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm. Energy, 36: 5568-5578.
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41. Dong, Y., W.C. Hong, Y. Xu and S. Yu, 2011. Selecting the individual numerical scale and the prioritization method in the analytic hierarchy process: A 2-tuple fuzzy linguistic approach. IEEE Trans. Fuzzy Syst., 19: 13-25.
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42. Lin, C.T., W.C. Hong, Y.F. Chen and Y. Dong, 2010. Application of salesman-like recommendation system in 3G mobile phone online shopping decision support. Expert Syst. Applic., 37: 8065-8078.
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43. Hong, W.C., Y. Dong, L.Y. Chen and C.Y. Lai, 2010. Taiwanese 3G mobile phone demand forecasting by SVR with hybrid evolutionary algorithms. Exp. Syst. Applic., 37: 4452-4462.
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44. Hong, W.C., 2010. Application of chaotic ant swarm optimization in electric load forecasting. Energy Policy, 38: 5830-5839.
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45. Dong, Y., G. Zhang, W.C. Hong and Y. Xu, 2010. Consensus models for AHP group decision making under row geometric mean prioritization method. Decis. Support Syst., 49: 281-289.
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46. Hung, W.M. and W.C. Hong, 2009. Application of SVR with improved ant colony optimization algorithms in exchange rate forecasting. Control Cybernetics, 38: 863-891.
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47. Hong, W.C., 2009. Hybrid evolutionary algorithms in a SVR-based electric load forecasting model. Int. J. Electrical Power Energy Syst., 31: 409-417.
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48. Hong, W.C., 2009. Global competitiveness measurement for the tourism sector. Curr. Issues Tourism, 12: 105-132.
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49. Hong, W.C., 2009. Electric load forecasting by support vector model. Applied Math. Mod., 33: 2444-2454.
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50. Hong, W.C., 2009. Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model. Energy Conversion Manage., 50: 105-117.
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51. Hong, W.C., 2008. Rainfall forecasting by technological machine learning models. Applied Math. Comput., 200: 41-57.
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52. Hong, W.C. and C.Y. Lai, 2008. Reliability forecasting by recurrent support vector regression. Int. J. Artificial Intell. Soft Comput., 1: 114-129.
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53. Chiang, H.W., 2008. Competitiveness in the tourism sector: A comprehensive approach from economic and management points. Physica-Verlag. Heidelberg. Germany.ISBN:13: 978-3790820416.
54. Pai, P.F. and W.C. Hong, 2007. A recurrent support vector regression model in rainfall forecasting. Hydrol. Process., 21: 819-827.
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55. Hong, W.C., Y.J. Lai, P.F. Pai and S.L. Yang, 2007. An improved support vector model in car-rental revenue forecast. J. Stat. Manage. Syst., 10: 427-437.
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56. Hong, W.C., S.Y. Wei and Y.F. Chen, 2007. A Comparative test of two employee turnover prediction models. Int. J. Manage., 24: 808-821.
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57. Hong, W.C., P.F. Pai, S.L. Yang and C.Y. Lai, 2007. Continuous ant colony optimization in a SVR urban traffic forecasting model. Lecture Notes Comput. Sci., 4507: 765-773.
58. Hong, W.C., C.T. Chen, S.L. Lee, P.W. Chen and Y.H. Yeh, 2007. The development of a framework for selecting a management information system. Int. J. Manage., 24: 790-801.
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59. Hong, W.C. and P.F. Pai, 2007. Potential assessment of the support vector regression technique in rainfall forecasting. Water Resour. Manage., 21: 495-513.
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60. Pai, P.F., W.C. Hong, P.T. Chang and C.T. Chen, 2006. The application of support vector machines to forecast tourist arrivals in Barbados: An empirical study. Int. J. Manage., 23: 375-385.
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61. Pai, P.F., C.S. Lin, W.C. Hong and C.T. Chen, 2006. A hybrid support vector machine regression for exchange rate prediction. Int. J. Inform. Manage. Sci., 17: 19-32.
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62. Pai, P.F. and W.C. Hong, 2006. Software reliability forecasting by support vector machines with simulated annealing algorithms. J. Syst. Software, 79: 747-755.
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63. Hong, W.C. and P.F. Pai, 2006. Predicting engine reliability by support vector machines. Int. J. Adv. Manuf. Technol., 28: 154-161.
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64. Pai, P.F., W.C. Hong and Y.S. Lee, 2005. Determining parameters of support vector machines by genetic algorithms-applications to reliability prediction. Int. J. Operations Res., 2: 1-7.
65. Pai, P.F., W.C. Hong and C.S. Lin, 2005. Forecasting tourism demand using a multifactor support vector machine model. Lecture Notes Comput. Sci., 3801: 512-519.
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66. Pai, P.F., W.C. Hong and C.S. Lin, 2005. Forecasting electric load by support vector machines with genetic algorithms. J. Adv. Computational Intell. Intell. Inform., 9: 134-141.
67. Pai, P.F., P.T. Chang, K.P. Lin and W.C. Hong, 2005. Hybrid learning fuzzy neural models in stock price forecasting. J. Inform. Optimization Sci., 26: 495-508.
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68. Pai, P.F. and W.C. Hong, 2005. Support vector machines with simulated annealing algorithms in electricity load forecasting. Energy Conversion Manage., 46: 2669-2688.
69. Pai, P.F. and W.C. Hong, 2005. Forecasting regional electric load based on recurrent support vector machines with genetic algorithms. Electric Power Syst. Res., 74: 417-425.
70. Pai, P.F. and W.C. Hong, 2005. An improved neural network model in forecasting arrivals. Ann. Tourism Res., 32: 1138-1141.
71. Hong, W.C., P.F. Pai, Y.Y. Huang and S.L. Yang, 2005. Application of support vector machines in predicting employee turnover based on job-performance. Lecture Notes Comput. Sci., 3610: 668-674.
72. Hong, W.C., P.F. Pai, C.T. Chen and P.T. Chang, 2005. Recurrent support vector machines with genetic algorithms in reliability prediction. Lecture Notes Comput. Sci., 3610: 619-629.