Cauchy mutation based on objective variable of Gaussian particle swarm optimization for parameters selection of SVM

On the basis of the slow convergence of particle swarm algorithm (PSO) during parameters selection of support vector machine (SVM), this paper proposes a hybrid mutation strategy that integrates Gaussian mutation operator and Cauchy mutation operator for PSO. The combinatorial mutation based on the fitness function value and the iterative variable is also applied to inertia weight. The results of application in parameter selection of support vector machine show the proposed PSO with hybrid mutation strategy based on Gaussian mutation and Cauchy mutation is feasible and effective, and the comparison between the method proposed in this paper and other ones is also given, which proves this method is better than sole Gaussian mutation and standard PSO.

[1]  Dong Guo Shao,et al.  A novel evolution strategy for multiobjective optimization problem , 2005 .

[2]  Leandro dos Santos Coelho,et al.  Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems , 2010, Expert Syst. Appl..

[3]  Rob Law,et al.  Complex system fault diagnosis based on a fuzzy robust wavelet support vector classifier and an adaptive Gaussian particle swarm optimization , 2010, Inf. Sci..

[4]  Qi Wu,et al.  Power load forecasts based on hybrid PSO with Gaussian and adaptive mutation and Wv-SVM , 2010, Expert Syst. Appl..

[5]  Wei-Chiang Hong,et al.  Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model , 2009 .

[6]  Leandro dos Santos Coelho,et al.  Solving economic load dispatch problems in power systems using chaotic and Gaussian particle swarm optimization approaches , 2008 .

[7]  Qi Wu,et al.  A hybrid-forecasting model based on Gaussian support vector machine and chaotic particle swarm optimization , 2010, Expert Syst. Appl..

[8]  Mingjun Wang,et al.  Particle swarm optimization-based support vector machine for forecasting dissolved gases content in power transformer oil , 2009 .

[9]  M. R. AlRashidi,et al.  LONG TERM ELECTRIC LOAD FORECASTING BASED ON PARTICLE SWARM OPTIMIZATION , 2010 .

[10]  Ling Zhuang,et al.  Prediction of silicon content in hot metal using support vector regression based on chaos particle swarm optimization , 2009, Expert Syst. Appl..

[11]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[12]  Shih-Wei Lin,et al.  Particle swarm optimization for parameter determination and feature selection of support vector machines , 2008, Expert Syst. Appl..

[13]  Wei Kong,et al.  A combination of modified particle swarm optimization algorithm and support vector machine for gene selection and tumor classification. , 2007, Talanta.

[14]  Dongxiao Niu,et al.  Middle-long power load forecasting based on particle swarm optimization , 2009, Comput. Math. Appl..

[15]  Shunde Yin,et al.  Geomechanical parameters identification by particle swarm optimization and support vector machine , 2009 .

[16]  X. C. Guo,et al.  A novel LS-SVMs hyper-parameter selection based on particle swarm optimization , 2008, Neurocomputing.

[17]  Moncef Gabbouj,et al.  Evolutionary artificial neural networks by multi-dimensional particle swarm optimization , 2009, Neural Networks.

[18]  Yutaka Maeda,et al.  On simultaneous perturbation particle swarm optimization , 2006, 2009 IEEE Congress on Evolutionary Computation.

[19]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[20]  Sheng-Fa Yuan,et al.  Fault diagnostics based on particle swarm optimisation and support vector machines , 2007 .

[21]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.