Abstract
We describe in this paper a Parallel Particle Swarm Optimization (PPSO) method with dynamic parameter adaptation to optimize complex mathematical functions. Fuzzy Logic is used to adapt the parameters of the PSO in the best way possible. The PPSO is shown to be superior to the individual evolutionary methods on the set of benchmark functions.
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References
Man, K.F., Tang, K.S., Kwong, S.: Genetic Algorithms: Concepts and Designs. Springer (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, 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. The Emerging Science of Artificial Life, p. 114. Princeton University Press (1994)
Angeline, P.J.: Using Selection to Improve Particle Swarm Optimization. In: Proceedings 1998 IEEE World Congress on Computational Intelligence, pp. 84–89. IEEE, Anchorage (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 (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)
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)
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Valdez, F., Melin, P., Castillo, O. (2013). Parallel Particle Swarm Optimization with Parameters Adaptation Using Fuzzy Logic. In: Batyrshin, I., Mendoza, M.G. (eds) Advances in Computational Intelligence. MICAI 2012. Lecture Notes in Computer Science(), vol 7630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37798-3_33
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DOI: https://doi.org/10.1007/978-3-642-37798-3_33
Publisher Name: Springer, Berlin, Heidelberg
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