Hybrid forecasting model based on support vector machine and particle swarm optimization with adaptive and Cauchy mutation

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• 作者：Qi Wu ; ^a ; ^b ; ^{wuqi7812@163.com} ; ^{hmwuqi@polyu.edu.hk}
• 关键词：Particle swarm optimization ; Cauchy mutation ; Support vector machine ; Hybrid forecasting model
• 刊名：Expert Systems with Applications
• 出版年：2011
• 出版时间：August 2011
• 年：2011
• 卷：38
• 期：8
• 页码：9070-9075
• 全文大小：453 K

文摘

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.