Volume 10 Number 11 (Nov. 2015)
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JSW 2015 Vol.10(11): 1301-1309 ISSN: 1796-217X
doi: 10.17706/jsw.10.11.1301-1309

Parameters Optimization of SVM Based on Improved FOA and Its Application in Fault Diagnosis

Qiantu Zhang1*, Liqing Fang1, Sicai Su2, Yan Lv2
1First Department, Mechanical Engineering College, Shijiazhuang, Hebei Province, P. R. China.
2Fourth Department, Mechanical Engineering College, Shijiazhuang, Hebei Province, P. R. China.


Abstract—In most cases, fault diagnosis is essentially a pattern recognition problem and support vector machine (SVM) provides a new solution for the diagnosis problem of systems in which the fault samples are few. However, the parameters selection in SVM has significant influence on the diagnosis performance. In this paper, improved fruit fly optimization algorithm (IFOA), which is basically the standard fruit fly optimization algorithm (FOA) combined with Levy flight search strategy, is proposed to determine the SVM parameters. Some benchmark datasets are used to evaluate the proposed algorithm. Furthermore, the proposed method is used to diagnose the faults of hydraulic pump. Experiments and engineering application show that the proposed method outperforms standard FOA, genetic algorithm (GA) and particle swarm optimization (PSO) methods.

Index Terms—SVM, fruit fly optimization algorithm, Levy flight, fault diagnosis.

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Cite: Qiantu Zhang, Liqing Fang, Sicai Su, Yan Lv, "Parameters Optimization of SVM Based on Improved FOA and Its Application in Fault Diagnosis," Journal of Software vol. 10, no. 11, pp. 1301-1309, 2015.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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