From d505e02a9c43bce3ac03437a386fba7ad2cfa286 Mon Sep 17 00:00:00 2001 From: coolneng Date: Tue, 22 Jun 2021 22:25:09 +0200 Subject: [PATCH] Add Summary --- docs/Summary.org | 161 ++++++++++++++++++++++++++++++++++++++ docs/Summary.pdf | Bin 0 -> 637639 bytes docs/assets/annealing.png | Bin 0 -> 147686 bytes 3 files changed, 161 insertions(+) create mode 100644 docs/Summary.org create mode 100644 docs/Summary.pdf create mode 100644 docs/assets/annealing.png diff --git a/docs/Summary.org b/docs/Summary.org new file mode 100644 index 0000000..04c63d4 --- /dev/null +++ b/docs/Summary.org @@ -0,0 +1,161 @@ +#+TITLE: Práctica 3 +#+SUBTITLE: Metaheurísticas +#+AUTHOR: Amin Kasrou Aouam +#+DATE: 2021-06-22 +#+PANDOC_OPTIONS: template:~/.pandoc/templates/eisvogel.latex +#+PANDOC_OPTIONS: listings:t +#+PANDOC_OPTIONS: toc:t +#+PANDOC_METADATA: lang=es +#+PANDOC_METADATA: titlepage:t +#+PANDOC_METADATA: listings-no-page-break:t +#+PANDOC_METADATA: toc-own-page:t +#+PANDOC_METADATA: table-use-row-colors:t +#+PANDOC_METADATA: colorlinks:t +#+PANDOC_METADATA: logo:/home/coolneng/Photos/Logos/UGR.png +#+LaTeX_HEADER: \usepackage[ruled, lined, linesnumbered, commentsnumbered, longend]{algorithm2e} +* Práctica 3 + +** Introducción + +En esta práctica, usaremos distintos algoritmos de búsqueda, basados en trayectorias, para resolver el problema de la máxima diversidad (MDP). Implementaremos: + +- Enfriamiento simulado +- Búsqueda multiarranque + +** Algoritmos + +*** Enfriamiento simulado + +El enfriamiento simulado es un algoritmo de búsqueda basado en trayectorias, con un criterio probabilístico basado en la termodinámica. + +El procedimiento general del algoritmo queda ilustrado a continuación: + +\begin{algorithm} + \KwIn{A list $[a_i]$, $i=1, 2, \cdots, n$, that contains the components of a solution} + \KwOut{Processed list} + + $currentSol \leftarrow initialize()$ + + $T0 \leftarrow initialize()$ + + $Tf \leftarrow initialize()$ + + $bestSol \leftarrow currentSol$ + + $T \leftarrow Tf$ + + \While{$T > Tf$}{ + $neighbour \leftarrow generateNeighbour(currentSol)$ + + $delta \leftarrow fitness(neighbour) - fitness(currentSol)$ + + \If{$delta > 0 \vee acceptNeighbour(neighbour)$}{ + $currentSol \leftarrow neighbour$ + + \If{$fitness(currentSol) > fitness(bestSol)$}{ + $bestSol \leftarrow currentSol$ + } + } + $coolDown(T)$ + } + \KwRet{$bestSolution$} +\end{algorithm} + +*** Búsqueda multiarranque + +La búsqueda multiarranque es un algoritmo que itera las 2 etapas siguientes: + +1. Generación de una solución inicial +2. Búsqueda local + +Es una variante simple de implementar a partir de una búsqueda local + +El procedimiento general del algoritmo queda ilustrado a continuación: + +\begin{algorithm} + \KwIn{A list $[a_i]$, $i=1, 2, \cdots, m$, the solution} + \KwOut{Processed list} + + $Solutions = [\ ]$ + + $initialSolutions \leftarrow getRandomSolutions()$ + + $Solutions.append(firstSolution)$ + + $maxIterations \leftarrow 1000$ + + \For{$i \leftarrow 0$ \KwTo $maxIterations$}{ + \For{$element in initialSolutions$}{ + $lastSolution \leftarrow getLastElement(initialSolutions)$ + + \While{$neighbour \leq lastSolution$}{ + $neighbour \leftarrow getNeighbouringSolution(lastSolution)$ + + $Solutions.append(neighbour)$ + + $lastSolution \leftarrow getLastElement(neighbour)$ + } + + $finalSolution \leftarrow getLastElement(Solutions)$ + } + } + \KwRet{$finalSolution$} +\end{algorithm} + +** Implementación + +La práctica ha sido implementada en /Python/, usando las siguientes bibliotecas: + +- NumPy +- Pandas + +\clearpage + +*** Instalación + +Para ejecutar el programa es preciso instalar Python, junto con las bibliotecas *Pandas* y *NumPy*. + +Se proporciona el archivo shell.nix para facilitar la instalación de las dependencias, con el gestor de paquetes [[https://nixos.org/][Nix]]. Tras instalar la herramienta Nix, únicamente habría que ejecutar el siguiente comando en la raíz del proyecto: + +#+begin_src shell +nix-shell +#+end_src + +** Ejecución + +La ejecución del programa se realiza mediante el siguiente comando: + +#+begin_src shell +python src/main.py +#+end_src + +Los parámetros posibles son: + +| dataset | algoritmo | +| Cualquier archivo de la carpeta data | annealing | +| | multistart | + +También se proporciona un script que ejecuta 1 iteración de cada algoritmo, sobre 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 +#+end_src + +*Nota*: se precisa instalar la biblioteca [[https://xlsxwriter.readthedocs.io/][XlsxWriter]] para la exportación de los resultados a un archivo Excel. + +* Análisis de los resultados +** Enfriamiento simulado + +#+CAPTION: Enfriamiento simulado +[[./assets/annealing.png]] + +El tiempo de ejecución varía ligeramente según el dataset: + +- Dataset con n=500: 13 segundos +- Dataset con n=2000: 38-39 segundos + +La distancia total obtenida, toma valores muy diferentes según el dataset en el que se ejecuta. + +** Búsqueda multiarranque + +Desafortunadamente, un /bug/ en el script de ejecución ha hecho que la segunda pestaña de la hoja de cálculo únicamente tenga el valor de los datasets. diff --git a/docs/Summary.pdf b/docs/Summary.pdf new file mode 100644 index 0000000000000000000000000000000000000000..62896950de526d5a71f23aa878a6a27ee2ebd869 GIT binary patch literal 637639 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