Application of Support Vector Machines to Accelerate the Solution Speed of Metaheuristic Algorithms

The support vector machine (SVM) is proposed as a response surface model to accelerate the solution speed of metaheuristic algorithms in solving inverse problems. The detail formulations of the SVM regression model using epsiv-insensitive loss function are derived. Primary numerical results are repo...

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Veröffentlicht in:	IEEE transactions on magnetics 2009-03, Vol.45 (3), p.1502-1505
Hauptverfasser:	Shiyou Yang, Liu, Q.H., Junwei Lu, Ho, S.L., Guangzheng Ni, Peihong Ni, Suming Xiong
Format:	Artikel
Sprache:	eng
Schlagworte:	Acceleration Algorithm design and analysis Algorithms Cross-disciplinary physics: materials science rheology Design engineering Exact sciences and technology Feasibility Inverse problem Inverse problems Least squares approximation Machine learning Magnetism Materials science Mathematical analysis Mathematical models metaheuristic algorithm Neural networks Other topics in materials science Physics Regression Response surface methodology response surface model Response surfaces Robustness support vector machine (SVM) Support vector machine classification Support vector machines
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Zusammenfassung:	The support vector machine (SVM) is proposed as a response surface model to accelerate the solution speed of metaheuristic algorithms in solving inverse problems. The detail formulations of the SVM regression model using epsiv-insensitive loss function are derived. Primary numerical results are reported to demonstrate the feasibility, performance, and robustness of the proposed SVM based response surface model for solving both mathematical functions and engineering design problems.
ISSN:	0018-9464 1941-0069
DOI:	10.1109/TMAG.2009.2012690