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

Authors:
Qi Wu
Affiliations:
Key Laboratory of Measurement and Control of CSE (School of Automation, Southeast University), Ministry of Education, Nanjing, Jiangsu 210096, China and School of Hotel and Tourism Management, Hon ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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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.