Abstract
This paper describes a hybrid approach for optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic to integrate the results, the proposed method is called FPSO+FGA. The new hybrid FPSO+FGA approach is compared with the Simulated Annealing (SA), PSO, GA, Pattern Search (PS) methods with a set of benchmark mathematical functions.
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
Man, K.F., Tang, K.S., Kwong, S.: Genetic Algorithms: Concepts and Designs. Springer, Heidelberg (1999)
Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micromachine and Human Science, Nagoya, Japan, pp. 39–43 (1995)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)
Holland, J.H.: Adaptation in natural and artificial system. The University of Michigan Press, Ann Arbor (1975)
Valdez, F., Melin, P.: Parallel Evolutionary Computing using a cluster for Mathematical Function Optimization, Nafips, San Diego CA, USA, pp. 598–602 (June 2007)
Castillo, O., Melin, P.: Hybrid intelligent systems for time series prediction using neural networks, fuzzy logic, and fractal theory. IEEE Transactions on Neural Networks 13(6), 1395–1408 (2002)
Fogel, D.B.: An introduction to simulated evolutionary optimization. IEEE Transactions on Neural Networks 5(1), 3–14 (1994)
Goldberg, D.: Genetic Algorithms. Addison Wesley (1988)
Emmeche, C.: Garden in the Machine. In: The Emerging Science of Artificial Life, p. 114. Princeton University Press (1994)
Valdez, F., Melin, P.: Parallel Evolutionary Computing using a cluster for Mathematical Function Optimization, Nafips, San Diego CA, USA, pp. 598–602 (June 2007)
Angeline, P.J.: Using Selection to Improve Particle Swarm Optimization. In: Proceedings 1998 IEEE World Congress on Computational Intelligence, Anchorage, Alaska, pp. 84–89. IEEE (1998)
Back, T., Fogel, D.B., Michalewicz, Z. (eds.): Handbook of Evolutionary Computation. Oxford University Press (1997)
Montiel, O., Castillo, O., Melin, P., Rodriguez, A., Sepulveda, R.: Human evolutionary model: A new approach to optimization. Inf. Sci. 177(10), 2075–2098 (2007)
Castillo, O., Valdez, F., Melin, P.: Hierarchical Genetic Algorithms for topology optimization in fuzzy control systems. International Journal of General Systems 36(5), 575–591 (2007)
Kim, D., Hirota, K.: Vector control for loss minimization of induction motor using GA–PSO. Applied Soft Computing 8, 1692–1702 (2008)
Liu, H., Abraham, A.: Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm.Article in press, Future Generation Computer Systems
Mohammed, O., Ali, S., Koh, P., Chong, K.: Design a PID Controller of BLDC Motor by Using Hybrid Genetic-Immune. Modern Applied Science 5(1) (February 2011)
Kirkpatrick, S., Gelatt, C.J., Vecchi, M.: Optimization by Simulated Annealing. Science 220(4598), 671–680 (1983)
Valdez, F., Melin, P., Castillo, O.: An improved evolutionary method with fuzzy logic for combining Particle Swarm Optimization and Genetic Algorithms. Appl. Soft Comput. 11(2), 2625–2632 (2011)
Hooke, R., Jeeves, T.A.: ’Direct search’ solution of numerical and statistical problems. Journal of the Association for Computing Machinery 8(2), 212–229 (1961)
Davidon, W.C.: Variable metric method for minimization. SIAM Journal on Optimization 1(1), 1–17 (1991)
Ochoa, A., Ponce, J., Hernández, A., Li, L.: Resolution of a Combinatorial Problem using Cultural Algorithms. JCP 4(8), 738–741 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Valdez, F., Melin, P., Castillo, O. (2011). Bio-Inspired Optimization Methods for Minimization of Complex Mathematical Functions. In: Batyrshin, I., Sidorov, G. (eds) Advances in Soft Computing. MICAI 2011. Lecture Notes in Computer Science(), vol 7095. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25330-0_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-25330-0_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25329-4
Online ISBN: 978-3-642-25330-0
eBook Packages: Computer ScienceComputer Science (R0)