Abstract
We describe in this paper an approach for mathematical function optimization using fuzzy logic for parameter tuning combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs). The proposed method combines the advantages of PSO and GA to give us an improved FPSO+FGA hybrid method. Fuzzy logic is helpful to find the optimal parameters in PSO and GA in the best way possible. Also, with the tuning of parameters based on fuzzy logic it is possible to balance the exploration and exploitation of the proposed method. The hybrid method is called FPSO+FGA and was tested with a set of benchmark mathematical functions.
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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, Reading (1988)
Emmeche, C.: Garden in the Machine. The Emerging Science of Artificial Life, p. 114. Princeton University Press, Princeton (1994)
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, Los Alamitos (1998)
Angeline, P.J.: Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences. In: Porto, V.W., Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 601–610. Springer, Heidelberg (1998)
Back, T., Fogel, D.B., Michalewicz, Z. (eds.): Handbook of Evolutionary Computation. Oxford University Press, Oxford (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. Future Generation Computer Systems (article in press)
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Valdez, F., Melin, P., Castillo, O. (2010). Fuzzy Logic for Parameter Tuning in Evolutionary Computation and Bio-inspired Methods. In: Sidorov, G., Hernández Aguirre, A., Reyes GarcÃa, C.A. (eds) Advances in Soft Computing. MICAI 2010. Lecture Notes in Computer Science(), vol 6438. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16773-7_40
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DOI: https://doi.org/10.1007/978-3-642-16773-7_40
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