Abstract
This paper deals with hybrid system (GA-BF) based on the conventional GA (Genetic Algorithm) and BF (Bacterial Foraging) which is social foraging behavior of bacteria for AVR system. This approach provides us with novel hybrid model based on foraging behavior and with also a possible new connection between evolutionary forces in social foraging and distributed nongradient optimization algorithm design for global optimization over noisy surfaces for AVR system.
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Lin, C.-L., Su, H.-W.: Intelligent control theory in guidance and control system design:an Overview. Proc. Natul. Sci., Counc. ROC(A) 24(1), 15–30 (2000)
Fleming, P.J., Purshouse, R.C.: Evolutionary algorithms in control system engineering: A survey. Control Eng. Practice 10, 1223–1241 (2002)
Dotoli, M., Maione, G., Naso, D., Turchiano, E.B.: Genetic identification of dynamical systems with static nonlinearities. In: Proc. IEEE SMCia 2001,Mountain Workshop Soft Computing Industrial Applications, Blacksburg, VA, June 25-27, pp. 65–70 (2001)
Gray, G.J., Murray-Smith, D.J., Li, Y., Sharman, K.C., Weinbrenner, T.: Nonlinear model structure identification using genetic programming. Contr. Eng. Practice (6), 1341–1352 (1998)
Kristinnson, K., Dumont, G.A.: System identification and control using genetic algorithms. IEEE Trans. System, Man, Cybern. 22, 1033–1046 (1992)
Krohling, R.A., Rey, J.P.: Design of optimal disturbance rejection PID controllers using genetic algorithms. IEEE Trans. Evol. Comput. 5, 78–82 (2001)
Gaing, Z.-L.: A Particle Swarm Optimization Approach for Optimum Design of PID Controller in AVR System. IEEE Trans. Energy Con. 19(2), 384–391 (2004)
Stephens, D.W., Krebs, J.R.: Foraging Theory. Princeton University Press, Princeton (1986)
Alcock, J.: Animal Behavior, .An Evolutionary Approach, Sinauer Associates. Sunderland, Massachusetts (1998)
Bell, W.J.: Searching Behavior. In: The Behavioral Ecology of Finding Resources, Chapman and Hall, London (1991)
Kim, D.H.: Robust tuning of PID controller with disturbance rejection using bacterial foraging based optimization. In: International symposium on computational intelligent and industrial application (ISCIIA2004), Hikou, China, December 20-22 (2004)
Kim, D.H.: Robust PID controller tuning using multiobjective optimization based on clonal selection of immune algorithm. In: Proc. Int. Conf. Knowledge-based intelligent information and engineering systems, pp. 50–56. Springer, Heidelberg (2004)
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Kim, D.H., Cho, J.H. (2005). Intelligent Control of AVR System Using GA-BF. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3684. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11554028_119
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DOI: https://doi.org/10.1007/11554028_119
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28897-8
Online ISBN: 978-3-540-31997-9
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