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An ACO model for a non-stationary formulation of the single elevator problem
Keywords:
Ant Colony Optimization (ACO), Single Elevator Problem (SEP), Non-stationary Problems, Ant Colony System designAbstract
The Ant Colony Optimization (ACO) metaheuristic is a bio-inspired approach for hard combinatorial optimization problems for stationary and non-stationary environments. In the ACO metaheuristic, a colony of artificial ants cooperate for finding high quality solutions in a reasonable time. An interesting example of a non-stationary combinatorial optimization problem is the Multiple Elevators Problem (MEP) which consists in finding a sequence of movements for each elevator to perform in a building so that to minimize, for instance, the users waiting average time. Events like the arrival of one new user to the elevator queue or the fault of one elevator dynamically produce changes of state in this problem. A subclass of MEP is the the so called Single Elevator Problem (SEP). In this work, we propose the design of an ACO model for the SEP that can be implemented as an Ant Colony System (ACS). Keywords: Ant Colony Optimization, Single Elevator Problem, Non-stationary Problems, Ant Colony System design.
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References
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[2] T. Eguchi, K. Hirasawa, Jinglu Hu, and S. Markon. Elevator group supervisory control systems using genetic network programming. In Congress on Evolutionary Computation, 2004. CEC2004, volume 2, pages 1661–1667, 19-23, June 2004.
[3] A. Fujino, T. Tobita, K. Segawa, K. Yoneda, and A. Togawa. An elevator group control system with floor-attribute control method and system optimization using genetic algorithms. In IEEE Transactions on Industrial Electronics, volume 44, pages 546–552, Aug. 1997.
[4] R. Gudwin, F. Gomide, and M. Andrade Netto. A fuzzy elevator group controller with linear context adaptation. In IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence, pages 481–486, 4-9 May 1998.
[5] Ming Ho and B. Robertson. Elevator group supervisory control using fuzzy logic. volume 2, pages 825–828, 25-28 Sept.
[6] N. Imasaki, S. Kubo, S. Nakai, T. Yoshitsugu, Jun-Ichi Kiji, and T. Endo. Elevator group control system tuned by a fuzzy neural network aplied method. In Proceedings of 1995 IEEE International Conference on Fuzzy Systems, 1995. International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium, volume 4, pages 1735–1740, 20-24 March 1995.
[7] Dorigo Marco and St ̈utzle Thomas. Ant Colony Optimization. Mit Press, 2004.
[8] Rafael Torres M ́arquez. Algoritmos evolutivos distribuidos en entornos din ́amicos. Master’s thesis, Departamento Lenguajes y Ciencias de la Computación - Universidad de Málaga, 2000.
[9] Silvia M. Molina, M. Guillermo Leguizamón, and Enrique Alba. Un Modelo ACO para una Versión No Estacionaria del Problema del Ascensor Único. In XII Congreso Argentino de Ciencias de la Computación (CACiC), San Luis, Argentina, 2006. Universidad Nacional de San Luis.
[10] J. Rambaud and P. Friese. On line-optimization of a multi-elevator transport system with reoptimization algorithms based on set-partitioning models. Technical report, ZIB Report 05-03, 2003.
[11] T. Tobita, A. Fujino, K. Segawa, K. Yoneda, and Y. Ichikawa. A parameter tuning method using genetic algorithms for an elevator group control system. In Proceedings of the 1996 IEEE IECON 22nd International Conference on Industrial Electronics, Control, and Instrumentation, pages 823–828, 5-10 Aug. 1996.
[12] J. Watada, S. Kojima, S. Ueda, and O. Ono. DNA computing approach to optimal decision problems. In International Joint Conference on Neural Networks - IEEE Conference on Fuzzy Systems, pages 25–29, Budapest, Hungary, July 2004.
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Published
2007-03-01
How to Cite
Molina, S., Leguizamón, M. G., & Alba Torres, E. (2007). An ACO model for a non-stationary formulation of the single elevator problem. Journal of Computer Science and Technology, 7(01), p. 41–51. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/802
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