Fluctuating wind velocity forecasting of hybridizing ant colony and particle swarm optimization based LSSVM
Li Chunxiang1, Ding xiaoda1, Ye Jihong2
1. Department of Civil Engineering, Shanghai University, Shanghai 200072;
2. Key Laboratory of Concrete and Prestressed Concrete Structures of the Ministry of Education, Southeast University, Nanjing 210018
In order to enhance the prediction accuracy of Least Square Support Vector Machine (LSSVM) for the fluctuating wind velocity, the hybridizing ant colony and particle swarm optimization（ACO and PSO）based LSSVM has been proposed in this paper. A two-stage meta-heuristic optimization framework is introduced to find the optimal parameters of LSSVM. In the first stage, the global search in the parameter space is accomplished using the ACO, realizing the preliminary optimization of the parameters of LSSVM. In the second phase, initializing particle swarm particle position with the first phase results and then, the further optimization is implemented through the PSO, thus obtaining more accurate LSSVM. Employing this hybrid intelligent optimum LSSVM, the fluctuating wind is predicted and compared with the results with the ACO and PSO based LSSVM, respectively. The numerical analysis shows that the proposed method can promote the prediction accuracy and the robust of LSSVM, thus having good engineering application prospects.
李春祥1，丁晓达1,叶继红2. 基于混合蚁群和粒子群优化LSSVM的脉动风速预测[J]. 振动与冲击, 2016, 35(21): 131-136.
Li Chunxiang1, Ding xiaoda1, Ye Jihong2. Fluctuating wind velocity forecasting of hybridizing ant colony and particle swarm optimization based LSSVM. JOURNAL OF VIBRATION AND SHOCK, 2016, 35(21): 131-136.
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