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R-chaosoptimiser: an optimiser for unconstrained global nonlinear optimisation written in R language for statistical computing
R-chaosoptimiser: un optimizador global no lineal sin restricciones escrito en lenguaje R para el calculo estadístico
DOI:
https://doi.org/10.15446/ing.investig.v31n3.26383Keywords:
optimisation, R language, gradient-based method, chaos, algorithm (en)optimización, Lenguaje R, métodos basados en gradiente, caos, algoritmos (es)
This paper discusses using R-chaosoptimiser, an R language package for nonlinear optimisation based on gradient techniques and chaos optimisation algorithms. Its implementation was based on three building blocks which could be executed alone or in combination: the first carrier wave algorithm, the chaos-based cyclical coordinate search method and the second wave carrier algorithm. Using chaos optimisation algorithms allows the tool to break away from local optimal points and converge towards an overall optimum inside a predefined search domain. Within the previous components, a user would be specifying the BFGS algorithm for refining the current best solution. Using the BFGS algorithm is not mandatory, so that its implementation was able to optimise problems having objective function discontinuities. However, the BFGS algorithm is a powerful local search method, meaning that it is used to exploit current knowledge about an objective function for improving a current solution; an explanatory example is presented.
En este artículo se discute la implementación de rchaosoptimizer, un paquete de R para la optimización no lineal basada en técnicas de gradiente y algoritmos de optimización caóticos. La implementación está basada en tres bloques constructivos que pueden ser ejecutados solos o combinados: 1) el algoritmo de primera onda; 2) el método de búsqueda por coordenadas cíclicas basado en caos; y, 3) el algoritmo de segunda onda. El uso de algoritmos de optimización caóticos permite a la herramienta implementada escapar de puntos óptimos locales y converger al óptimo global dentro del domino predefinido de búsqueda. Dentro de los componentes previos, el usuario podría especificar una llamada al algoritmo BFGS para refinar la solución actual. El uso del algoritmo BFGS no es obligatorio, tal que la implementación es capaz de optimizar problemas con discontinuidades en la función objetivo. Sin embargo, el algoritmo BFGS es un método poderoso de búsqueda local, tal que, él es usado para explotar el conocimiento sobre la función objetivo para mejorar la solución actual. Finalmente, en ejemplo exploratorio es presentado.
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Copyright (c) 2011 Juan David Velásquez H.
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