Efecto de los coeficientes de aceleración de PSO en el desempeño de una Red Neuronal Artificial aplicada a la Estimación de Costos
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 performinga comparative study...
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Universidad Nacional de Chimborazo
2018-06-01
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| Series: | NOVASINERGIA |
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| Online Access: | http://novasinergia.unach.edu.ec/index.php/novasinergia/article/view/23/5 |
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| author | Elba Bodero Poveda Guillermo Leguizamón |
| author_facet | Elba Bodero Poveda Guillermo Leguizamón |
| author_sort | Elba Bodero Poveda |
| collection | DOAJ |
| description | 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 performinga comparative study of the effect of the acceleration coefficients c1and 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 c1and c2obtain 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 |
| format | Article |
| id | doaj-art-8eeb0e6b77f8410f9f28291a7dd4cfc8 |
| institution | OA Journals |
| issn | 2631-2654 |
| language | English |
| publishDate | 2018-06-01 |
| publisher | Universidad Nacional de Chimborazo |
| record_format | Article |
| series | NOVASINERGIA |
| spelling | doaj-art-8eeb0e6b77f8410f9f28291a7dd4cfc82025-08-20T01:54:53ZengUniversidad Nacional de ChimborazoNOVASINERGIA2631-26542018-06-01113340Efecto de los coeficientes de aceleración de PSO en el desempeño de una Red Neuronal Artificial aplicada a la Estimación de CostosElba Bodero Poveda0Guillermo Leguizamón1Universidad Nacional de La Plata, ArgentinaUniversidad Nacional de San Luis, ArgentinaThe 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 performinga comparative study of the effect of the acceleration coefficients c1and 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 c1and c2obtain 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 0.5http://novasinergia.unach.edu.ec/index.php/novasinergia/article/view/23/5Coeficientes de Aceleración PSOEstimación de CostosMetaheurística PoblacionalParticle Swarm OptimizationRed Neuronal Artificial |
| spellingShingle | Elba Bodero Poveda Guillermo Leguizamón Efecto de los coeficientes de aceleración de PSO en el desempeño de una Red Neuronal Artificial aplicada a la Estimación de Costos NOVASINERGIA Coeficientes de Aceleración PSO Estimación de Costos Metaheurística Poblacional Particle Swarm Optimization Red Neuronal Artificial |
| title | Efecto de los coeficientes de aceleración de PSO en el desempeño de una Red Neuronal Artificial aplicada a la Estimación de Costos |
| title_full | Efecto de los coeficientes de aceleración de PSO en el desempeño de una Red Neuronal Artificial aplicada a la Estimación de Costos |
| title_fullStr | Efecto de los coeficientes de aceleración de PSO en el desempeño de una Red Neuronal Artificial aplicada a la Estimación de Costos |
| title_full_unstemmed | Efecto de los coeficientes de aceleración de PSO en el desempeño de una Red Neuronal Artificial aplicada a la Estimación de Costos |
| title_short | Efecto de los coeficientes de aceleración de PSO en el desempeño de una Red Neuronal Artificial aplicada a la Estimación de Costos |
| title_sort | efecto de los coeficientes de aceleracion de pso en el desempeno de una red neuronal artificial aplicada a la estimacion de costos |
| topic | Coeficientes de Aceleración PSO Estimación de Costos Metaheurística Poblacional Particle Swarm Optimization Red Neuronal Artificial |
| url | http://novasinergia.unach.edu.ec/index.php/novasinergia/article/view/23/5 |
| work_keys_str_mv | AT elbaboderopoveda efectodeloscoeficientesdeaceleraciondepsoeneldesempenodeunaredneuronalartificialaplicadaalaestimaciondecostos AT guillermoleguizamon efectodeloscoeficientesdeaceleraciondepsoeneldesempenodeunaredneuronalartificialaplicadaalaestimaciondecostos |