{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T00:04:54Z","timestamp":1774051494981,"version":"3.50.1"},"reference-count":32,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2015,4,7]],"date-time":"2015-04-07T00:00:00Z","timestamp":1428364800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,4,7]]},"abstract":"\n Purpose<\/jats:title>\n \u2013 To accurately forecast logistics freight volume plays a vital part in rational planning formulation for a country. The purpose of this paper is to contribute to developing a novel combination forecasting model to predict China\u2019s logistics freight volume, in which an improved PSO-BP neural network is proposed to determine the combination weights. <\/jats:p>\n <\/jats:sec>\n \n Design\/methodology\/approach<\/jats:title>\n \u2013 Since BP neural network has the ability of learning, storing, and recalling information that given by individual forecasting models, it is effective in determining the combination weights of combination forecasting model. First, an improved PSO based on simulated annealing method and space-time adjustment strategy (SAPSO) is proposed to solve out the connection weights of BP neural network, which overcomes the problems of local optimum traps, low precision and poor convergence during BP neural network training process. Then, a novel combination forecast model based on SAPSO-BP neural network is established. <\/jats:p>\n <\/jats:sec>\n \n Findings<\/jats:title>\n \u2013 Simulation tests prove that the proposed SAPSO has better convergence performance and more stability. At the same time, combination forecasting models based on three types of BP neural networks are developed, which rank as SAPSO-BP, PSO-BP and BP in accordance with mean absolute percentage error (MAPE) and convergent speed. Also the proposed combination model based on SAPSO-BP shows its superiority, compared with some other combination weight assignment methods. <\/jats:p>\n <\/jats:sec>\n \n Originality\/value<\/jats:title>\n \u2013 SAPSO-BP neural network is an original contribution to the combination weight assignment methods of combination forecasting model, which has better convergence performance and more stability.<\/jats:p>\n <\/jats:sec>","DOI":"10.1108\/k-09-2014-0201","type":"journal-article","created":{"date-parts":[[2015,6,8]],"date-time":"2015-06-08T08:55:27Z","timestamp":1433753727000},"page":"646-666","source":"Crossref","is-referenced-by-count":15,"title":["Adaptive combination forecasting model for China\u2019s logistics freight volume based on an improved PSO-BP neural network"],"prefix":"10.1108","volume":"44","author":[{"given":"Zhou","family":"Cheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Juncheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2020122422153811100_b1","doi-asserted-by":"crossref","unstructured":"Arani, B.O.\n , \n Mirzabeygi, P.\n and \n Panahi, M.S.\n (2013), \u201cAn improved PSO algorithm with a territorial diversity-preserving scheme and enhanced exploration-exploitation balance\u201d, \n Swarm and Evolutionary Computation\n , Vol. 11 No. 3, pp. 1-15.","DOI":"10.1016\/j.swevo.2012.12.004"},{"key":"key2020122422153811100_b2","doi-asserted-by":"crossref","unstructured":"Bates, J.M.\n and \n Granger, C.W.\n (1969), \u201cThe combination of forecasts\u201d, \n Operations Research\n , Vol. 20 No. 4, pp. 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