Ajuste posinomial combinando Enjambre de Partículas y Métodos de Programación No-lineal//Posynomial fitting combiding Particle Swarm and Nonlinear Programming Methods

Authors

  • Nabote António-Magaia Instituto Superior Politécnico de Songo. Tete.

Abstract

El desempeño de los métodos determinísticos de programación no-lineal es altamente sensible de la selección de la aproximación inicial, en particular cuando se trata de la obtención de un óptimo global, como es requerido en el ajuste posinomial. El presente trabajo tuvo como objetivo, generar la aproximación inicial a través de la metaheurística enjambre de partículas, para mejorar el desempeño de los métodos de programación no-lineal en el ajuste posinomial. A partir de pruebas no paramétricas que se realizaron para la comparación de los resultados obtenidos en los problemas considerados, se observó que las diferencias existentes entre los resultados del ajuste posinomial, con y sin la aplicación de la propuesta de mejora, son significativas. Además, se constató que la metodología de mejora desarrollada permite obtener un buen desempeño de los métodos de programación no-lineal en el ajuste posinomial, lo que redunda en la obtención de modelos posinomiales de calidad. 

Palabras claves: ajuste posinomial, programación no-lineal, optimización por enjambre de partículas, programación geométrica.
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Abstract

The performance of deterministic methods of nonlinear programming is highly sensitive to the selection of the initial approximation, particularly when it comes to obtaining an overall optimum, as required in the posyinomial fitting. The present work aimed to generate the initial approximation through of particle swarm metaheuristic, to improve the performance of non-linear programming methods in posyinomial fitting. From the non-parametric tests performed for the comparison of the results obtained in the considered problems, it was observed that the differences between the results of the posyinomial fitting, with and without the application of the improvement proposal, are significant. In addition, it was found that the improvement methodology developed allows to obtain a good performance of the non-linear programming methods in the posynomial fitting, which results in the obtaining of quality posynomial models.

Key words: posynomial fitting, nonlinear programming, particle swam optimization, geometric programming.

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Published

2017-07-14

How to Cite

1.
António-Magaia N. Ajuste posinomial combinando Enjambre de Partículas y Métodos de Programación No-lineal//Posynomial fitting combiding Particle Swarm and Nonlinear Programming Methods. Ing. Mec. [Internet]. 2017 Jul. 14 [cited 2025 Sep. 13];20(2):83-90. Available from: https://ingenieriamecanica.cujae.edu.cu/index.php/revistaim/article/view/562

Issue

Vol. 20 No. 2 (2017): mayo-agosto

Section

Original article

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