Quan Liang
School of Economics and Management, Shijiazhuang Tiedao University, Hebei, Shijiazhuang, 050043, China
Li-Yan Geng
School of Economics and Management, Shijiazhuang Tiedao University, Hebei, Shijiazhuang, 050043, China
ABSTRACT
Regional logistics demand forecasting plays a crucial role in regional logistics infrastructure construction and regional economic development. Least Squares Support Vector Machines (LSSVM) has been widely applied to forecasting regional logistics demand. One of the main problems in LSSVM forecasting is the randomness and subjectivity of the parameters selection in LSSVM, which leads to the poor forecasting performance on regional logistics demand. To overcome the problem, this paper, integrating an Improved Particle Swarm Optimization (IPSO) algorithm into LSSVM, proposed a regional logistics demand forecasting model based on LSSVM-IPSO in which the IPSO algorithm was adopted to optimize the parameters of LSSVM and the LSSVM with the optimal parameters was used to forecast regional logistics demand. An example on Hebei logistics demand forecasting was taken to verify the effectiveness of the proposed model. The results show that IPSO algorithm effectively enhances the regional logistics demand forecasting accuracy of LSSVM model.
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How to cite this article
Quan Liang and Li-Yan Geng, 2013. Regional Logistics Demand Forecasting Based on Lssvm with Improved Particle
Swarm Optimization Algorithm. Information Technology Journal, 12: 7854-7858.
DOI: 10.3923/itj.2013.7854.7858
URL: https://scialert.net/abstract/?doi=itj.2013.7854.7858
DOI: 10.3923/itj.2013.7854.7858
URL: https://scialert.net/abstract/?doi=itj.2013.7854.7858
REFERENCES
- Adrangi, B., A. Chatrath and K. Raffiee, 2001. The demand for US air transport service: A chaos and nonlinearity investigation. Transp. Res. E, 37: 337-353.
CrossRef - Chaturvedi, K.T., M. Pandit and L. Srivastava, 2009. Particle swarm optimization with time varying acceleration coefficients for non-convex economic power dispatch. Int. J. Electr. Power Energy Syst., 31: 249-257.
CrossRef - Fite, J.T., G.D. Taylor, J.S. Usher, J.R. English and J.N. Roberts, 2002. Forecasting freight demand using economic indices. Int. J. Phys. Distrib. Logistics Manage., 34: 299-308.
CrossRefDirect Link - Huang, M.Z. and Y.B. Feng, 2009. The grey prediction model applies to regional logistics demand forecasting. Logist. Technol., 32: 17-20.
CrossRef - Suykens, J.A.K. and J. Vandewalle, 1999. Least squares support vector machine classifiers. Neural Process. Lett., 9: 293-300.
CrossRefDirect Link - Xu, H.B. and G.H. Chen, 2013. An intelligent fault identification method of rolling bearings based on LSSVM optimized by improved PSO. Mech. Syst. Signal Process, 35: 167-175.
CrossRef