Flower pollination algorithm approach for the transportation energy demand estimation in Turkey: model development and application

References

• Abdel-Basset, M., and L. A. Shawky. 2018. Flower pollination algorithm: a comprehensive review. Artificial Intelligence Review 1–25. doi:.10.1007/s10462-018-9624-4.
   Google Scholar
• Abdel-Raouf, O., I. El-Henawy, and M. Abdel-Baset. 2014. A novel hybrid flower pollination algorithm with chaotic harmony search for solving sudoku puzzles. International Journal of Modern Education and Computer Science 6 (3):38. doi:10.5815/ijmecs.2014.03.05.
   Google Scholar
• Al-Ghandoor, A., M. Samhouri, I. Al-Hinti, J. Jaber, and M. Al-Rawashdeh. 2012. Projection of future transport energy demand of Jordan using adaptive neuro-fuzzy technique. Energy 38 (1):128–35. doi:10.1016/j.energy.2011.12.023.
   Web of Science ®Google Scholar
• Arora, S., and P. Anand. 2017. Chaos-enhanced flower pollination algorithms for global optimization. Journal of Intelligent & Fuzzy Systems 33 (6):3853–69. doi:10.3233/JIFS-17708.
   Web of Science ®Google Scholar
• B.I.T.R.E. Bureau of Infrastructure, Transport and Regional Economics, 2012, Traffic Growth: Modelling a Global Phenomenon, Report 128, Canberra ACT, Australia.
   Google Scholar
• Baskan, O., S. Haldenbilen, H. Ceylan, and H. Ceylan. 2012. Estimating transport energy demand using ant colony optimization. Energy Sources, Part B: Economics, Planning, and Policy 7 (2):188–99. doi:10.1080/15567240903030513.
   Web of Science ®Google Scholar
• Behrang, M., E. Assareh, M. Assari, and A. Ghanbarzadeh. 2011. Total energy demand estimation in Iran using bees algorithm. Energy Sources, Part B: Economics, Planning, and Policy 6 (3):294–303. doi:10.1080/15567240903502594.
   Web of Science ®Google Scholar
• Beskirli, M., H. Hakli, and H. Kodaz. 2017. The energy demand estimation for Turkey using differential evolution algorithm. Sādhanā 42 1–11.
   Web of Science ®Google Scholar
• Bozorg-Haddad, O. 2018. Advanced optimization by nature-inspired algorithms. . Singapore: Springer.
   Google Scholar
• Canyurt, O. E., H. K. Ozturk, A. Hepbasli, and Z. Utlu. 2006. Genetic algorithm (GA) approaches for the transport energy demand estimation: Model development and application. Energy Sources, Part A 28 (15):1405–13. doi:10.1080/15567030600917033.
   Web of Science ®Google Scholar
• Ceylan, H., H. Ceylan, S. Haldenbilen, and O. Baskan. 2008. Transport energy modeling with meta-heuristic harmony search algorithm, an application to Turkey. Energy Policy 36 (7):2527–35. doi:10.1016/j.enpol.2008.03.019.
   Web of Science ®Google Scholar
• de Castro, L. N. 2007. Fundamentals of natural computing: An overview. Physics of Life Reviews 4 (1):1–36. doi:10.1016/j.plrev.2006.10.002.
   Web of Science ®Google Scholar
• Demir, M. 2013. The relationship between energy import and current account deficit: The case of Turkey with var analysis. Journal of Academic Researches and Studies 5 (9):2–27.
   Google Scholar
• El-Henawy, I., and M. Ismail. 2014. An improved chaotic flower pollination algorithm for solving large integer programming problems. International Journal of Digital Content Technology and Its Applications 8 (3):72.
   Google Scholar
• F. Motasemi, Afzal, M T., Salema, A A., Moghavvemi, M., Shekarchian, M., Zarifi, F., Mohsin, R. 2014. Energy and exergy utilization efficiencies and emission performance of Canadian transportation sector, 1990–2035. Energy 64:355–66. doi:10.1016/j.energy.2013.09.064.
   Web of Science ®Google Scholar
• Frank E., M. Hall, G. Holmes, R. Kirkby, B. Pfahringer, I. H. Witten. and L Trigg. 2009. Weka-a machine learning workbench for data mining. In, Maimon O., Rokach L. (eds). Data mining and knowledge discovery handbook, 1269–77, Springer, Boston, MA.
   Google Scholar
• G. D. E. A. Republic of Turkey Ministry of Energy and Natural Resources. 2016. Balance Sheets: http://www.turkstat.gov.tr
   Google Scholar
• G. D. H. Genaral directorate of highways. 2016. Highway transportation statistics. http://www.eigm.gov.tr/en-US/MainPage
   Google Scholar
• Garner, S. R. 1995. Weka: The waikato environment for knowledge analysis. Proceedings of the New Zealand computer science research students conference, pp. 57–64, Hamilton, New Zealand.
   Google Scholar
• Haldenbilen, S., and H. Ceylan. 2005. Genetic algorithm approach to estimate transport energy demand in Turkey. Energy Policy 33 (1):89–98. doi:10.1016/S0301-4215(03)00202-7.
   Web of Science ®Google Scholar
• Hyndman, R. J., and A. B. Koehler. 2006. Another look at measures of forecast accuracy. International Journal of Forecasting 22 (4):679–88. doi:10.1016/j.ijforecast.2006.03.001.
   Web of Science ®Google Scholar
• IMF. 2017. World economic outlook database october 2017. https://www.imf.org/external/pubs/ft/weo/2017/02/weodata/index.aspx
   Google Scholar
• Johansson, Å., Y. Guillemette, F. Murtin, D. Turner, G. Nicoletti, C. de la Maisonneuve, G. Bousquet, and F. Spinelli. 2012. Looking to 2060: long-term global growth prospects: a going for growth report. . Paris: OECD Publishing.
   Google Scholar
• Kalra, S., and S. Arora. 2016. Firefly algorithm hybridized with flower pollination algorithm for multimodal functions. Proceedings of the International Congress on Information and Communication Technology, Springer, Singapore, pp. 207–19
   Google Scholar
• Kaveh, A., N. Shamsapour, R. Sheikholeslami, and M. Mashhadian. 2012. Forecasting transport energy demand in Iran using meta-heuristic algorithms. International Journal of Optimization in Civil Engineering 2 (4):533–44.
   Google Scholar
• Limanond, T., S. Jomnonkwao, and A. Srikaew. 2011. Projection of future transport energy demand of Thailand. Energy Policy 39 (5):2754–63. doi:10.1016/j.enpol.2011.02.045.
   Web of Science ®Google Scholar
• Lu, I., C. Lewis, and S. J. Lin. 2009. The forecast of motor vehicle, energy demand and CO 2 emission from Taiwan’s road transportation sector. Energy Policy 37 (8):2952–61. doi:10.1016/j.enpol.2009.03.039.
   Web of Science ®Google Scholar
• Łukasik, S., and P. A. Kowalski. 2015. Study of flower pollination algorithm for continuous optimization. In: Angelov P. et al. (eds) Intelligent systems’ 2014, 451–59. Springer, Cham.
   Google Scholar
• Murat, Y. S., and H. Ceylan. 2006. Use of artificial neural networks for transport energy demand modeling. Energy Policy 34 (17):3165–72. doi:10.1016/j.enpol.2005.02.010.
   Web of Science ®Google Scholar
• Nabil, E. 2016. A modified flower pollination algorithm for global optimization. Expert Systems with Applications 57:192–203. doi:10.1016/j.eswa.2016.03.047.
   Web of Science ®Google Scholar
• Özceylan, E. 2016. Forecasting CO2 emission of Turkey: Swarm intelligence approaches. International Journal of Global Warming 9 (3):337–61. doi:10.1504/IJGW.2016.075450.
   Web of Science ®Google Scholar
• Sakib, N., M. W. U. Kabir, M. Subbir, and S. Alam. 2014. A comparative study of flower pollination algorithm and bat algorithm on continuous optimization problems. International Journal of Soft Computing and Engineering 4 (3):13–19.
   Google Scholar
• Shabbir, R., and S. S. Ahmad. 2010. Monitoring urban transport air pollution and energy demand in Rawalpindi and Islamabad using leap model. Energy 35 (5):2323–32. doi:10.1016/j.energy.2010.02.025.
   Web of Science ®Google Scholar
• Sharawi, M., E. Emary, I. A. Saroit, and H. El-Mahdy. 2014. Flower pollination optimization algorithm for wireless sensor network lifetime global optimization. International Journal of Soft Computing and Engineering 4 (3):54–59.
   Google Scholar
• Sonmez, M., A. P. Akgüngör, and S. Bektaş. 2017. Estimating transportation energy demand in Turkey using the artificial bee colony algorithm. Energy 122:301–10. doi:10.1016/j.energy.2017.01.074.
   Web of Science ®Google Scholar
• Sözen, A., and E. Arcaklioglu. 2007. Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey. Energy Policy 35 (10):4981–92. doi:10.1016/j.enpol.2007.04.029.
   Web of Science ®Google Scholar
• Taylan, O., and A. Demirbas. 2016. Forecasting and analysis of energy consumption for transportation in the kingdom of Saudi Arabia. Energy Sources, Part B: Economics, Planning, and Policy 11 (12):1150–57. doi:10.1080/15567249.2015.1004383.
   Web of Science ®Google Scholar
• M.F.A.. 2017. Republic of Turkey ministry of foreign affairs economic outlook of Turkey. http://www.mfa.gov.tr/prospects-and-recent-developments-in-the-turkish-economy.en.mfa
   Google Scholar
• WB. 0000. World Bank Data. https://data.worldbank.org/country/turkey
   Google Scholar
• Witten, I. H., E. Frank, L. E. Trigg, and S. J. Cunningham. 1999. Weka: practical machine learning tools and techniques with java implementations. (Working paper 99/11). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
   Google Scholar
• Yang, X.-S. 2010a. Nature-inspired metaheuristic algorithms. . United Kingdom: Luniver press.
   Google Scholar
• Yang, X.-S. 2010b. Engineering optimization: an introduction with metaheuristic applications. NJ: John Wiley & Sons.
   Google Scholar
• Yang, X.-S. 2012a. Flower pollination algorithm for global optimization. International conference on unconventional computing and natural computation, Berlin, Heidelberg, Springer. pp. 240–49.
   Google Scholar
• Yang X.-S. 2012b Flower Pollination Algorithm for Global Optimization. In: Durand-Lose J., Jonoska N. (eds) Unconventional Computation and Natural Computation. UCNC 2012. Lecture Notes in Computer Science, vol 7445. Springer, Berlin, Heidelberg.
   Google Scholar
• Zhang, M., H. Mu, G. Li, and Y. Ning. 2009. Forecasting the transport energy demand based on PLSR method in China. Energy 34 (9):1396–400. doi:10.1016/j.energy.2009.06.032.
   Web of Science ®Google Scholar