From daf3af574cbd0ed6e7f096c3ee9d8c4385496aea Mon Sep 17 00:00:00 2001 From: coolneng Date: Sat, 22 May 2021 10:46:23 +0200 Subject: [PATCH] Update Summary using new algorithm results --- docs/Summary.org | 22 +++++++++++----------- docs/Summary.pdf | Bin 756969 -> 779587 bytes docs/algorithm-results.xlsx | Bin 9199 -> 8889 bytes docs/assets/greedy.png | Bin 134304 -> 144378 bytes docs/assets/local.png | Bin 137508 -> 158305 bytes 5 files changed, 11 insertions(+), 11 deletions(-) diff --git a/docs/Summary.org b/docs/Summary.org index c856432..1d61ef5 100644 --- a/docs/Summary.org +++ b/docs/Summary.org @@ -116,7 +116,7 @@ Los parámetros posibles son: | Cualquier archivo de la carpeta data | greedy | | | local | -También se proporciona un script que ejecuta 3 iteraciones de ambos algoritmos, con cada uno de los /datasets/, y guarda los resultados en una hoja de cálculo. Se puede ejecutar mediante el siguiente comando: +También se proporciona un script que ejecuta 1 iteración del algoritmo greedy y 3 iteraciones de la búsqueda local, con cada uno de los /datasets/, y guarda los resultados en una hoja de cálculo. Se puede ejecutar mediante el siguiente comando: #+begin_src shell python src/execution.py @@ -133,23 +133,23 @@ Los resultados obtenidos se encuentran en el archivo /algorithm-results.xlsx/, p #+CAPTION: Algoritmo greedy [[./assets/greedy.png]] -El algoritmo greedy es determinista, por lo tanto la desviación típica es prácticamente nula (varía en el tiempo de ejecución únicamente). El tiempo de ejecución varía considerablemente según el dataset: +El algoritmo greedy es determinista, por lo tanto la desviación típica es nula, dado que se ejecuta una única vez. El tiempo de ejecución varía considerablemente según el dataset: -- Dataset con n=500: 8-10 segundos -- Dataset con n=2000: 6-7 minutos +- Dataset con n=500: 7-10 segundos +- Dataset con n=2000: 5-12 minutos -Por lo tanto, el algoritmo o la implementación de éste no escalan al aumentar el número de casos. - -Las distancias obtenidas son considerablemente peores que las del algoritmo de búsqueda local, aunque hay que tener en cuenta que en la implementación del algoritmo de búsqueda local la distancia del primer elemento no es 0, lo cual tiene afecta el resultado final. +La distancia total obtenida, por lo general, es inferior al algoritmo de búsqueda local, aunque no difiere significativamente. *** Algoritmo de búsqueda local #+CAPTION: Algoritmo de búsqueda local [[./assets/local.png]] -El algoritmo de búsqueda local es estocástico, debido a que para la obtención de cada una de las soluciones se utiliza un generador de números pseudoaleatorio. El tiempo de ejecución es prácticamente constante con cada dataset: +El algoritmo de búsqueda local es estocástico, debido a que para la obtención de cada una de las soluciones se utiliza un generador de números pseudoaleatorio. El tiempo de ejecución varía considerablemente según el dataset: -- Dataset con n=500: 1-3 segundos -- Dataset con n=2000: 8-12 segundos +- Dataset con n=500: 1-2 minutos +- Dataset con n=2000: 20-25 minutos -Por lo tanto éste escala bien al aumentar el número de casos. Aún así, al realizar ciertas pruebas aumentando el número de iteraciones máximas, el rendimiento del algoritmo emperoaba considerablemente. Por este motivo, las ejecuciones de este algoritmo se han hecho con 100 iteraciones máximas. +La distancia total obtenida, por lo general, es superior al algoritmo greedy lo cual indica que la búsqueda local obtiene mejores resultados a expensas del tiempo de ejecución. + +Debido a nuestras limitaciones computacionales, las ejecuciones de este algoritmo se hicieron con 100 iteraciones máximas. diff --git a/docs/Summary.pdf b/docs/Summary.pdf index f32e84a216a278db761e1ee850f180f94dbc44e4..3a1002e2852efaaba2357aa2793749aeab94eead 100644 GIT binary patch delta 310205 zcmbTdWmH^Uwzf?|AVETcTY%v1?g{Sh?hb`J?1bPN+#L#cw<5TEaEIXT4j;+W{dAwx zr{5nRqsCyc#@;~9z1E!fyyjgyiF6(P^j~~m_`a-$)>(bQ8LztvO$}Is=Gopq9_nUB zpvWkl;6!&Qo(ne{jNR>y^HrY2Y(3c1IO&F*A?d#8L97rZ-ee+i= zy?O^dUr-`D+jO0`c)phli@!)8SQ+|N2X|3kpslO(`mn67`KSx**x`r2|$i;VLdGntu~ z$s6{<3lWhup;w`iLIuOvPt=|!fuL4zUY)WPX18nPP}!K7yNgkxl6U+t$<=wA1-DWz zWCV-mGAo{7*U_a)0FJ`nvf0S+oZOMR8UBIP@@PyxP0Jx0nM(G1DT+UJkG&(zQiE%k 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