Fuzzy Controller Design for Ball and Beam System with an Improved Ant Colony Optimization
In this paper, an improved ant colony optimization
(ACO) algorithm is proposed to enhance the performance of global
optimum search. The strategy of the proposed algorithm has the
capability of fuzzy pheromone updating, adaptive parameter tuning,
and mechanism resetting. The proposed method is utilized to tune the
parameters of the fuzzy controller for a real beam and ball system.
Simulation and experimental results indicate that better performance
can be achieved compared to the conventional ACO algorithms in the
aspect of convergence speed and accuracy.
[1] M. Dorigo, L.M. Gambardella, "Ant colony system : a cooperative
learning approach to the traveling salesman problem, " IEEE Tran. on
Evolutionary Computation, vol. 1, no. 1, pp. 53-66, 1997.
[2] M. Dorigo, V. Maniezzo, and A. Colorni, "Ant system: optimization by a
colony of cooperating agents," IEEE Tran. on Systems, Man, and
Cybernetics, Part B: Cybernetics, vol. 26, no. 1, pp. 29-41, 1996.
[3] C. Blum, "Ant colony optimization: Introduction and recent trends, "
Physics of Life Reviews, vol. 2, no. 4, pp. 353-373, 2005.
[4] Y. Li and S. Gong, "Dynamic ant colony optimisation for TSP, " The
International Journal of Advanced Manufacturing Technology, vol. 22,
pp. 528-533, 2003.
[5] C.F. Tsai, C.W. Tsai, and C.C. Tseng, "A new hybrid heuristic approach
for sloving large travling salesman problem, " Information Sciences, vol.
166, no. 1, pp. 67-81, 2004.
[6] S.C. Negulescu, C.V. Kifor, and C. O, "Ant colony solving multiple
constrains problem: Vehicle route allocation, " International Journal of
Computers, Communications and Control, vol. 3, no. 4, pp. 366-373,
2008.
[7] J. Heinonen and F. pettersson, "Hybrid ant colony optimization and
visibility studies applied to a job-shop scheduling problem, " Applied
Mathematics and Computation, vol. 187, no. 2, pp. 989-998, 2007.
[8] L.Y. Tseng and S.C. Liang , "A hybrid metaheuristic for the quadratic
assignment problem," Computational Optimization and Applications, vol.
14, no. 1, pp. 85-113, 2006.
[9] S. Tsutsui, "Solving the quadratic assignment problems using parallel
ACO with symmetric multi processing, " Transactions of the Japanese
Society for Artificial Intelligence, vol. 24, no. 1, pp. 46-57, 2009.
[10] A.P. Engelbrecht, Computational Intelligence: An Introduction, 2nd,
Wiley, 2007.
[11] C. Martinez, O. Castillo, and O. Montiel, "Comparison between ant
colony and genetic algorithms for fuzzy system optimization, " Studies in
Computational Intelligence, vol. 154, no. 4, pp. 71-86, 2008.
[12] C.F. Juang and C. Lo, "Zero-order TSK-type fuzzy system learning using
a two-phase swarm intelligence algorithm, " Fuzzy Sets and Systems, vol.
159, no. 21, pp. 2910-2926, 2008.
[13] T. Stutzle, H.H. Hoos, "Max-Min ant system, " Future Generation
Computer Systems, vol. 16, no. 8, pp. 889-914, 2000.
[1] M. Dorigo, L.M. Gambardella, "Ant colony system : a cooperative
learning approach to the traveling salesman problem, " IEEE Tran. on
Evolutionary Computation, vol. 1, no. 1, pp. 53-66, 1997.
[2] M. Dorigo, V. Maniezzo, and A. Colorni, "Ant system: optimization by a
colony of cooperating agents," IEEE Tran. on Systems, Man, and
Cybernetics, Part B: Cybernetics, vol. 26, no. 1, pp. 29-41, 1996.
[3] C. Blum, "Ant colony optimization: Introduction and recent trends, "
Physics of Life Reviews, vol. 2, no. 4, pp. 353-373, 2005.
[4] Y. Li and S. Gong, "Dynamic ant colony optimisation for TSP, " The
International Journal of Advanced Manufacturing Technology, vol. 22,
pp. 528-533, 2003.
[5] C.F. Tsai, C.W. Tsai, and C.C. Tseng, "A new hybrid heuristic approach
for sloving large travling salesman problem, " Information Sciences, vol.
166, no. 1, pp. 67-81, 2004.
[6] S.C. Negulescu, C.V. Kifor, and C. O, "Ant colony solving multiple
constrains problem: Vehicle route allocation, " International Journal of
Computers, Communications and Control, vol. 3, no. 4, pp. 366-373,
2008.
[7] J. Heinonen and F. pettersson, "Hybrid ant colony optimization and
visibility studies applied to a job-shop scheduling problem, " Applied
Mathematics and Computation, vol. 187, no. 2, pp. 989-998, 2007.
[8] L.Y. Tseng and S.C. Liang , "A hybrid metaheuristic for the quadratic
assignment problem," Computational Optimization and Applications, vol.
14, no. 1, pp. 85-113, 2006.
[9] S. Tsutsui, "Solving the quadratic assignment problems using parallel
ACO with symmetric multi processing, " Transactions of the Japanese
Society for Artificial Intelligence, vol. 24, no. 1, pp. 46-57, 2009.
[10] A.P. Engelbrecht, Computational Intelligence: An Introduction, 2nd,
Wiley, 2007.
[11] C. Martinez, O. Castillo, and O. Montiel, "Comparison between ant
colony and genetic algorithms for fuzzy system optimization, " Studies in
Computational Intelligence, vol. 154, no. 4, pp. 71-86, 2008.
[12] C.F. Juang and C. Lo, "Zero-order TSK-type fuzzy system learning using
a two-phase swarm intelligence algorithm, " Fuzzy Sets and Systems, vol.
159, no. 21, pp. 2910-2926, 2008.
[13] T. Stutzle, H.H. Hoos, "Max-Min ant system, " Future Generation
Computer Systems, vol. 16, no. 8, pp. 889-914, 2000.
@article{"International Journal of Electrical, Electronic and Communication Sciences:54788", author = "Yeong-Hwa Chang and Chia-Wen Chang and Hung-Wei Lin and C.W. Tao", title = "Fuzzy Controller Design for Ball and Beam System with an Improved Ant Colony Optimization", abstract = "In this paper, an improved ant colony optimization
(ACO) algorithm is proposed to enhance the performance of global
optimum search. The strategy of the proposed algorithm has the
capability of fuzzy pheromone updating, adaptive parameter tuning,
and mechanism resetting. The proposed method is utilized to tune the
parameters of the fuzzy controller for a real beam and ball system.
Simulation and experimental results indicate that better performance
can be achieved compared to the conventional ACO algorithms in the
aspect of convergence speed and accuracy.", keywords = "Ant colony algorithm, Fuzzy control, ball and beamsystem", volume = "3", number = "8", pages = "1567-6", }