| A new support vector machine optimized by improved particle swarm optimization and its application |
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| 作者姓名： | 李翔 杨尚东 乞建勋 |
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| 作者单位： | School of Business Administration, North China Electric Power University, Beijing 102206, China |
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| 摘 要： | A new support vector machine （SVM） optimized by an improved particle swarm optimization （PSO） combined with simulated annealing algorithm （SA） was proposed. By incorporating with the simulated annealing method, the global searching capacity of the particle swarm optimization（SAPSO） was enchanced, and the searching capacity of the particle swarm optimization was studied. Then, the improyed particle swarm optimization algorithm was used to optimize the parameters of SVM （c,σ and ε）. Based on the operational data provided by a regional power grid in north China, the method was used in the actual short term load forecasting. The results show that compared to the PSO-SVM and the traditional SVM, the average time of the proposed method in the experimental process reduces by 11.6 s and 31.1 s, and the precision of the proposed method increases by 1.24% and 3.18%, respectively. So, the improved method is better than the PSO-SVM and the traditional SVM.
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| 关 键 词： | 支持向量机 颗粒群优化算法 短期负载预测 模拟退火 |
| 文章编号： | 1005-9784（2006）05-568-05 |
| 收稿时间： | 2006-06-10 |
| 修稿时间： | 2006-07-16 |
A new support vector machine optimized by improved particle swarm optimization and its application |
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| Li Xiang , Yang Shang-dong and Qi Jian-xun.A new support vector machine optimized by improved particle swarm optimization and its application[J].Journal of Central South University of Technology,2006,13(5):568-572. |
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| Authors: | Li Xiang Yang Shang-dong and Qi Jian-xun |
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| Affiliation: | (1) School of Business Administration, North China Electric Power University, 102206 Beijing, China |
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| Abstract: | A new support vector machine (SVM) optimized by an improved particle swarm optimization (PSO) combined with simulated annealing
algorithm (SA) was proposed. By incorporating with the simulated annealing method, the global searching capacity of the particle
swarm optimization (SAPSO) was enhanced, and the searching capacity of the particle swarm optimization was studied. Then,
the improved particle swarm optimization algorithm was used to optimize the parameters of SVM (c, σ and ε). Based on the operational data provided by a regional power grid in north China, the method was used in the actual short
term load forecasting. The results show that compared to the PSO-SVM and the traditional SVM, the average time of the proposed
method in the experimental process reduces by 11.6 s and 31.1 s, and the precision of the proposed method increases by 1.24%
and 3.18%, respectively. So, the improved method is better than the PSO-SVM and the traditional SVM.
Foundation item: Project(50579101) supported by the National Natural Science Foundation of China |
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| Keywords: | support vector machine particle swarm optimization algorithm short-term load forecasting simulated annealing |
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