Effect of the PSO acceleration coefficients on the performance of an Artificial Neural Network applied to the Cost Estimation
DOI:
https://doi.org/10.37135/unach.ns.001.01.04Keywords:
PSO Acceleration Coefficients, Estimation of Costs, Population Metaheuristics, Particle Swarm Optimization, Artificial Neural NetworkAbstract
The particle metaheuristics Particle Swarm Optimization (PSO) since its appearance has proven to be efficient in solving optimization problems, the variation of its parameters has allowed to improve its efficiency. The present work is focused on performing a comparative study of the effect of the acceleration coefficients c1 and c2, on the performance of PSO to solve a problem of cost estimation, through an Artificial Neural Network (ANN) sigmoidal feedforward. A range of values was evaluated in the acceleration coefficients, the other parameters, in this case inertial factor and the swarm size were worked with fixed values. The validation of the solution was carried out by means of a pipeline data set for fluid transfer used in the industry, coming from a real case, with information related to weight, welding type, diameter and the corresponding cost. The objective function used is the Mean Square Error (MSE), calculated between the observed values and the values estimated by the ANN. From the results it can be seen that very small values of c1 and c2 obtain low accuracy in the estimation of pipe manufacturing costs, while the best accuracy is achieved by means of acceleration coefficients with values greater than or equal to 0.5.
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References
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- Barba, L. & Bodero, E. (2017). Redes Neuronales Artificiales para Estimación de Costos de Construcción Industrial. V Congreso Internacional de Investigación y Actualización en Ingenierías, Galápagos (Ecuador), 269-278.
- 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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Published
2018-06-12
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Research Articles and Reviews
How to Cite
Effect of the PSO acceleration coefficients on the performance of an Artificial Neural Network applied to the Cost Estimation. (2018). Novasinergia, ISSN 2631-2654, 1(1), 33-40. https://doi.org/10.37135/unach.ns.001.01.04
