Abstract
The generalization performance of LS-SVM depends on a good setting of its parameters. Chaotic particle swarm optimization (CPSO) with simulated annealing algorithm (SACPSO) is proposed to choose the parameters of LS-SVM automatically. CPSO adopts chaotic mapping with certainty, ergodicity and the stochastic property, possessing high search efficiency. SA algorithm employs certain probability to improve the ability of PSO to escape from a local optimum. The results show that the proposed approach has a better generalization performance and is more effective than LS-SVM based on particle swarm optimization.
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References
Vapnik, V.N.: The nature of statistical learning theory. Springer, New York (1995)
Suykens, J.A.K., Vandewalle, J.: Least squares support vector machine classifiers. Neural Processing Letters 9, 293–300 (1999)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceeding of the 1995 IEEE international conference on neural network, pp. 1942–1948 (1995)
Eberthart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceeding of the sixth international symposium on micro machine and human science, pp. 39–43 (1995)
Yi, D., Ge, X.R.: An improved PSO-based ANN with simulated annealing technique. Neuro-computing 63, 527–533 (2005)
Zhang, C.K., Shao, H.H.: An ANN’s evolved by a new evolutionary system and its application. In: Proceedings of the 39th IEEE conference on decision and control, Sydney, Australia, pp. 3562–3563 (2000)
Shi, Y., Eberhart, R.: Empirical study of particle swarm optimization. In: Proceedings of congress on evolutionary computation, pp. 1945–1950 (1999)
Jiang, C.W., Etorre, B.: A self-adaptive chaotic particle swarm algorithm for short term hydroelectric system scheduling in deregulated environment. Energy Conversion and Management 46, 2689–2696 (2005)
Eberhart, R., Shi, Y.: Particle swarm optimization: developments, applications and resources. In: Proceeding of the 2001 IEEE International Conference on evolutionary computation, pp. 81–86 (2001)
Balram, S.: Study of simulated annealing based algorithms for multiobjective optimization of a constrained problem. Computers and Chemical Engineering 28, 1849–1871 (2004)
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Chen, Al., Wu, Zm., Yang, Gk. (2006). LS-SVM Based on Chaotic Particle Swarm Optimization with Simulated Annealing. In: Cai, JY., Cooper, S.B., Li, A. (eds) Theory and Applications of Models of Computation. TAMC 2006. Lecture Notes in Computer Science, vol 3959. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11750321_9
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DOI: https://doi.org/10.1007/11750321_9
Publisher Name: Springer, Berlin, Heidelberg
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