Hybrid forecasting model based on support vector machine and particle swarm optimization with adaptive and Cauchy mutation |
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| Authors: | Qi Wu |
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| Affiliation: | 1. Department of Industrial Engineering, Jilin University, Changchun, China;2. Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan;1. School of Automation, Southeast University, Nanjing 210096, China;2. The Key Laboratory of Machine Perception (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China;3. The Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia;4. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;1. Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Avenue, Tehran, Iran;2. Faculty of Industrial & Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran;3. Department of Industrial Engineering, Sharif University of Technology, P.O. Box 11155-9414, Azadi Ave., Tehran 1458889694, Iran |
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| Abstract: | This paper presents a novel hybrid forecasting model based on support vector machine and particle swarm optimization with Cauchy mutation objective and decision-making variables. On the basis of the slow convergence of particle swarm algorithm (PSO) during parameters selection of support vector machine (SVM), the adaptive mutation operator based on the fitness function value and the iterative variable is also applied to inertia weight. Then, a hybrid PSO with adaptive and Cauchy mutation operator (ACPSO) is proposed. The results of application in regression estimation show the proposed hybrid model (ACPSO–SVM) 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 other methods. |
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