Efecto de los coeficientes de aceleración de PSO en el desempeño de una Red Neuronal Artificial aplicada a la Estimación de Costos
DOI:
https://doi.org/10.37135/unach.ns.001.01.04Palabras clave:
Coeficientes de Aceleración PSO, Estimación de Costos, Metaheurística Poblacional, Particle Swarm Optimization, Red Neuronal ArtificialResumen
La metaheurística poblacional Particle Swarm Optimization (PSO) desde su aparición ha demostrado ser eficiente en la solución de problemas de optimización, la variación de sus parámetros ha permitido mejorar su eficiencia. El presente trabajo está centrado en realizar un estudio comparativo del efecto de los coeficientes de aceleración, c1 y c2, en el desempeño de PSO para resolver un problema de estimación de costos por medio de una Red Neuronal Artificial (ANN) feedforward sigmoidal. Se evaluó un rango de valores en los coeficientes de aceleración, los demás parámetros, en este caso factor inercial y el tamaño de enjambre se trabajaron con valores fijos. La validación de la solución se realizó por medio de un conjunto de datos de fabricación de tuberías para transferencia de fluidos utilizada en la industria, proveniente de un caso real, con información relacionada con peso, tipo de soldadura, diámetro y el correspondiente costo. La función objetivo utilizada es el Error Cuadrático Medio (MSE), calculado entre los valores observados y los valores estimados por la ANN. A partir de los resultados se puede observar que valores muy pequeños de c1 y c2 obtienen baja exactitud en la estimación de costos de fabricación de tubería, en tanto que la mejor exactitud es lograda por medio de coeficientes de aceleración con valores mayores o iguales a 0.5.
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Referencias
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- Carlisle, A. & Dozier, G. (2001). An off-the-self PSO. Proceedings of the Workshop on Particle Swarm Optimization, Indianapolis (USA), 1-6.
- Chang, R. & Lu, C.N. (2002). Feeder reconfiguration for load factor improvement. Proceedings of the IEEE Power Engineering Society Winter Meeting, New York (USA), Vol. 2, 980-984.
- Duarte, C. & Quiroga, J. (2010). Algoritmo PSO para identificación de parámetros en un motor DC”. Revista Facultad de Ingeniería, Universidad de Antioquia, 55.
- Eberhart, R. & Hu, X. (1999). Human tremor analysis using particle swarm optimization. Proceedings of the 1999 Congress on Evolutionary Computation-CEC99, Washington (USA), Vol. 3, 1927-1930.
- Eberhart, R. & Shi, Y. (2001). Particle swarm optimization: developments, applications and resources. Proceedings of the 2001 Congress on Evolutionary Computation, Seoul (South Korea), Vol. 1, 81-86.
- Franken, N. & Engelbrecht, A. P. (2004). PSO approaches to co-evolve IPD strategies. Proceedings of the 2004 IEEE Congress on Evolutionary Computation, Oregon (USA), 356-363.
- Freeman, J. A. & Skapura, D. M. (1991). Algorithms, Applications, and Programming Techniques. USA: Addison-Wesley Publishing Company.
- Gaing, Z. L. (2003). Discrete particle swarm optimization algorithm for unit commitment. Proceedings of the 2003 IEEE Power Engineering Society General Meeting, Toronto (Canadá), Vol. 1, 418-424.
- Hu, X. & Eberhart, R. (2002). Adaptive particle swarm optimization: detection and response to dynamic systems. Proceedings of the 2002 Congress on Evolutionary Computation-CEC02, Honolulu (USA), Vol. 2, 1666-1670.
- Hu, X., Eberhart, R. & Shi, Y. (2003). Engineering optimization with particle swarm. Proceedings of the 2003 IEEE Swarm Intelligence Symposium, Indianapolis (USA), 53-57.
- Ismail, A. & Engelbrecht, A. (2000). Global optimization algorithms for training product unit neural networks. Proceedings of IEEE-INNS-ENNS International Joint Conference on Neural Networks, Como (Italy), Vol. 1, 132-137.
- Kennedy, J. (1997). The particle swarm: social adaptation of knowledge. Proceedings of the 1997 IEEE International Conference on Evolutionary Computation-ICEC97, Indianapolis (USA), 303-308.
- Kennedy, J. & Eberhart, R. (1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks-ICNN’95, Perth (Australia), Vol.4, 1942-1948.
- Kennedy, J. & Eberhart, R. (2001). Swarm intelligence. San Francisco: Morgan Kaufmann Publishers.
- Koay, C.A. & Srinivasan, D. (2003). Particle swarm optimization-based approach for generator maintenance scheduling. Proceedings of the 2003 IEEE Swarm Intelligence Symposium, Indianapolis (USA), 167-173.
- Laskari, E., Parsopoulos, K. & Vrahatis, M. (2002). Particle swarm optimization for minimax problems. Proceedings of the 2002 Congress on Evolutionary Computation-CEC02, Honolulu (USA), Vol. 2, 1576-1581.
- Lu, Z-S. & Yan, S. (2004). Multiuser detector based on particle swarm algorithm. Proceedings of the IEEE 6th CAS Symposium on Emerging Technologies: Frontiers of Mobile and Wireless Communication, Shanghai (China), 783-786.
- Millonas, M.M. (1994). Swarms, phase transitions, and collective intelligence. Proceedings of Artificial life III, Vol. XVII, SFI Studies in the Sciences of Complexity, Addison-Wesley.
- Miranda, V. & Fonseca, N. (2002). EPSO – Evolutionary particle swarm optimization, a new algorithm with applications in power systems. IEEE/PES Transmission and Distribution Conference and Exhibition, Porto Alegre (Brazil), Vol. 2, 745-750.
- Naka, S., Genji, T., Yura, T. & Fukuyama, Y. (2001). Practical distribution state estimation using hybrid particle swarm optimization. Proceedings of 2001 Winter Meeting of the IEEE Power Engineering Society, Columbus (USA), Vol. 2, 815-820.
- Naka, S., Genji, T., Yura, T. & Fukuyama, Y. (2003). A hybrid particle swarm optimization for distribution state estimation. IEEE Transactions on Power Systems, Vol. 18, No. 1, 60-68.
- Rodriguez, N. & Duran, O. (2013). Reduced Multivariate Polynomial Model for Manufacturing Costs Estimation of Piping Elements. Mathematical Problems in Engineering, Vol. 2013.
- Srinivasan, D., Loo, W. & Cheu, E. (2003). Traffic incident detection using particle swarm optimization. Proceedings of the 2003 IEEE Swarm Intelligence Symposium, Indianapolis (USA), 144-151.
- Tasgetiren, M.F. & Liang, Y-C. (2003). A binary particle swarm optimization algorithm for lot sizing problem. Journal of Economic and Social Research, Vol. 5, No. 2, 1-20.
- Veeramachaneni, K. & Osadciw, L.A. (2004a). Optimal scheduling in sensor networks using swarm intelligence. Proceedings of the Conference on Information Sciences and System, Princeton University, New Jersey (USA).
- Veeramachaneni, K. & Osadciw, L.A. (2004b). Dynamic sensor management using multi objective particle swam optimizer. Proceedings of the SPIE, Vol. 5434, Multisensor, Multisource Information Fusion: Architecture, Algorithms, and Applications 2004, 205-216.
- Vesterstrom, J. & Riget, J. (2002). Particle swarms: Extensions for improved local, multi-modal, and dynamic search in numerical optimization (Master’s Thesis). University of Aarhus.
- Wang, Z., Durst, G., Eberhart, R., Boyd, D. & Miled, Z. (2004). Particle swarm optimization and neural network application for QSAR. Proceedings of the 18th International Parallel and Distributed Processing Symposium (IPDPS’04), New Mexico, (USA).
- Yangyang, Z., Chunlin, J.I., Ping, Y., Manlin, L.I., Chaojin, W. & Guangxing, W. (2004). Particle swarm optimization for base station placement in mobile communication. Proceedings of the 2004 IEEE International Conference on Networking, Sensing & Control, Taipei (Taiwan), 428-432.
- Zhao, Y. & Zheng, J. (2004). Particle swarm optimization algorithm in signal detection and blind extraction. Proceedings of the 7th International Symposium on Parallel Architectures, Hong Kong (China), 37-41.
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Publicado
2018-06-12
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Artículos de Investigación y Artículos de Revisión
Cómo citar
Efecto de los coeficientes de aceleración de PSO en el desempeño de una Red Neuronal Artificial aplicada a la Estimación de Costos. (2018). Novasinergia, ISSN 2631-2654, 1(1), 33-40. https://doi.org/10.37135/unach.ns.001.01.04
