文章基本信息

标题：Bacterial Foraging Optimization Based on LS-SVM for BTP Forecasting
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作者：SONG Qiang ; GUO Xiao-bo ; LI hua 等
期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2016
卷号：9
期号：1
页码：387-396
DOI：10.14257/ijhit.2016.9.1.33
出版社：SERSC
摘要：Because of the nonlinear characteristics of the BTP in sintering process, the BTP forecasting is difficult to realize. The LS-SVM was employed in this study for forecasting. However, Because SVM is using the two programming support vector, computing and solving two quadratic programming will involve matrix of order m, when the M number is large storage and computing the matrix will consume a large amount of computer memory and calculation time. The traditional training methods based on searching technique are not effective and fast. Therefore, bacterial foraging optimization (BFO) was adopted to optimize the LS-SVM. BFO is a novel and powerful global search technique, It is found that Bacteria Foraging Algorithm (BFO) is capable of improving the speed of convergence as well as the precision in the desired result. Simulation results clearly illustrate that the proposed approach is very efficient and could easily be extended for other global optimization problems. It can conclude that BFO is effective and rapid for the cluster analysis problem.
关键词：Bacteria Foraging Algorithm (BFO); LS-SVM; Optimization; BTP