Fluctuating wind velocity forecasting of hybridizing ant colony and particle swarm optimization based LSSVM

PDF(2097 KB)

Journal of Vibration and Shock ›› 2016, Vol. 35 ›› Issue (21) : 131-136.

Li Chunxiang1, Ding xiaoda1, Ye Jihong2

Author information +

History +

Abstract

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.

Key words

Fluctuating wind velocity forecasting / Least Square Support Vector Machine / Hybrid intelligent optimization；Ant colony optimization / Particle swarm optimization

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Li Chunxiang1, Ding xiaoda1, Ye Jihong2. Fluctuating wind velocity forecasting of hybridizing ant colony and particle swarm optimization based LSSVM[J]. Journal of Vibration and Shock, 2016, 35(21): 131-136

References

[1] 黄本才. 结构抗风分析原理及应用[M]. 上海：同济大学出版社, 2008, 51-59.
Huang Ben-cai. The application and principle of structure preventing wind [M].Shanghai: Tongji University Press. 2008, 51-59.
[2] Shi Y, Liu H, Gao L, Zhang G. Cellular particle swarm optimization[J]. Information Sciences, 2011, 181: 4460-4493.
[3] Niknam T. An efficient hybrid evolutionary algorithm based on PSO and HBMO algorithms for multi-objective distribution feeder reconfiguration[J]. Energy Conversion and Management, 2009, 50: 2074-2082.
[4] Li Xiao-dong, Yao Xin. Cooperatively coevolving particle swarms for large scale optimization [J]. IEEE Transactions on Evolutionary Computation, 2012, 16(2): 210-224.
[5] Suykens JAK, Vandewalle J. Least squares support vector machine classifiers [J]. Neural Processing, 1999, 9(3): 293-300.
[6] Dorigo M, Sttzle T. The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances [M]. Kluwer Academic Publishers, 2002.
[7] Zlochin M, Birattari M, Meuleau N, Dorigo M. Model-based search for combinatorial optimization [R]. Technical Report TR/IRIDIA/2001-15.
[8] Kennedy J, Eberhart RC. Swarm Intelligence [M]. Morgan Kaufman Publishers, 2002.
[9] Mahani Amir Saber, Shojaee Saeed, Salajegheh Eysa, Khatibinia Mohsen. Hybridizing two-stage meta-heuristic optimization model with weighted least squares support vector machine for optimal shape of double-arch dams [J]. Applied Soft Computing, 2015, 27: 205-218.
[10] Shi Y, Eberhart RC. A modified particle swarm optimizer, in: Proceedings of IEEE International Conference on Evolutionary Computation[R], IEEE Press, 1998.
[11] 张弦, 王宏力. 基于粒子群优化的最小二乘支持向量机在时间序列预测中的应用[J].中国机械工程,2011,22(21): 2572-2576.
Zhang Xian, Wang Hong-li. The application of least squares support vector machine based on Particle Swarm Optimization in the forecasting of time series[j]. China Mechanical Engineering, 2011, 22(21): 2572-2576.