Abstract
A novel orthogonal simulated annealing algorithm OSA for optimizations of electromagnetic problems is proposed in this paper. High performance of OSA mainly arises from an intelligent generation mechanism (IGM) based on orthogonal experimental design (OED). The OED-based IGM can efficiently generate a good candidate solution for next move by using a systematic reasoning method instead of the conventional method of random perturbation. It is shown empirically that OSA performs well in solving parametric optimization problems and in designing optimal electromagnetic devices, compared with some existing optimization methods using simulated annealing algorithms and genetic algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Yang, S., Machado, J.-M., Ni, G., Ho, S.-L., Zhou, P.: A self-learning simulated annealing algorithm for global optimization of electromagnetic problems. IEEE Trans. Magnetics 37(5), 3579–3583 (2001)
Renyuan, T., Jianzhong, S., Yan, L.: Optimization of electromagnetic devices by using intelligent simulated annealing algorithm. IEEE Trans. Magnetics 34(5), 2992–2995 (1998)
Simkin, J., Trowbridge, C.-W.: Optimization electromagnetic devices combining direct search methods with simulated annealing. IEEE Trans. Magnetics 28(2), 1545–1548 (1992)
Kirkpatrick, S., Gelatt, C.-D., Vecchi, M.-P.: Optimization by simulated annealing. Science 220, 671–680 (1983)
Szu, H., Hartley, R.: Fast simulated annealing. Physics Getters 122, 157–162 (1987)
Tasllis, C., Stariolo, D.A.: Generalized simulated annealing. Physica A 233, 395–406 (1996)
Gong, G., Liu, Y., Qian, M.: An adaptive simulated annealing algorithm. Stochastic Processes andtheir Applications 94, 95–103 (2001)
Chen, X., Qia, J., Ni, G., Yang, S., Zhang, M.: An improved genetic algorithm for global optimization of electromagnetic problems. IEEE Trans. Magnetics 37(5), 3579–3583 (2001)
Renyuan, T., Shiyou, Y., Yan, L.: Combined strategy of improved simulated annealing and genetic algorithm for inverse problem. IEEE Trans. Magnetics 32(3), 1326–1329 (1996)
Leung, Y.W., Wang, Y.: An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Trans. Evol. Comput. 5(1), 41–53 (2001)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Mechanism Learning. Addison - Wesley Publishing Company, London (1989)
Geman, S., Geman, D.: Stochastic relaxation, gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Analysis and Machines Intelligence 6, 721–741 (1984)
Jianni, S.: Numerical Analysis of Electromagnetic Field. Academic, Beijing (1984)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shu, LS., Ho, SJ., Ho, SY. (2003). A Novel Orthogonal Simulated Annealing Algorithm for Optimization of Electromagnetic Problems. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_54
Download citation
DOI: https://doi.org/10.1007/978-3-540-45080-1_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40550-4
Online ISBN: 978-3-540-45080-1
eBook Packages: Springer Book Archive