From 94570601e189d5a3b8f761a90dc95e2cdf8a340a Mon Sep 17 00:00:00 2001 From: coolneng Date: Mon, 19 Apr 2021 22:15:57 +0200 Subject: [PATCH] Add algorithms pseudocode in the summary --- docs/Summary.org | 86 ++++++++++++++++++++++++++++++++++++ docs/Summary.pdf | Bin 0 -> 455914 bytes docs/Tablas_MDP_2020-21.xls | Bin 0 -> 35840 bytes 3 files changed, 86 insertions(+) create mode 100644 docs/Summary.org create mode 100644 docs/Summary.pdf create mode 100644 docs/Tablas_MDP_2020-21.xls diff --git a/docs/Summary.org b/docs/Summary.org new file mode 100644 index 0000000..d228a91 --- /dev/null +++ b/docs/Summary.org @@ -0,0 +1,86 @@ +#+TITLE: Práctica 1 +#+SUBTITLE: Metaheurísticas +#+AUTHOR: Amin Kasrou Aouam +#+DATE: 2021-04-19 +#+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: logo:/home/coolneng/Photos/Logos/UGR.png +#+LaTeX_HEADER: \usepackage[ruled, lined, linesnumbered, commentsnumbered, longend]{algorithm2e} +* Práctica 1 + +** Introducción + +En esta práctica, usaremos distintos algoritmos de búsqueda para resolver el problema de la máxima diversidad (MDP). Implementaremos: + +- Algoritmo /Greedy/ +- Algoritmo de búsqueda local + +** Algoritmos + +*** Greedy + +El algoritmo /greedy/ añade de forma iterativa un punto, hasta conseguir una solución de tamaño m. + +En primer lugar, seleccionamos el elemento más lejano de los demás (centroide), y lo añadimos en nuestro conjunto de elementos seleccionados. A éste, añadiremos en cada paso el elemento correspondiente según la medida del /MaxMin/. Ilustramos el algoritmo a continuación: + +\begin{algorithm} + \KwIn{A list $[a_i]$, $i=1, 2, \cdots, m$, that contains the chosen point and the distance} + \KwOut{Processed list} + + $Sel = [\ ]$ + + $centroid \leftarrow getFurthestElement()$ + + \For{$i \leftarrow 0$ \KwTo $m$}{ + \For{$element$ in $Sel$}{ + $closestElements = [\ ]$ + + $closestPoint \leftarrow getClosestPoint(element)$ + + $closestElements.append(closestPoint)$ + } + $maximum \leftarrow max(closestElements)$ + + $Sel.append(maximum)$ + } + \KwRet{$Sel$} +\end{algorithm} + +*** Búsqueda local + +El algoritmo de búsqueda local selecciona una solución aleatoria, de tamaño /m/, y explora durante un número máximo de iteraciones soluciones vecinas. + +Para mejorar la eficiencia del algoritmo, usamos la heurística del primer mejor (selección de la primera solución vecina que mejora la actual). Ilustramos el algoritmo a continuación: + +\begin{algorithm} + \KwIn{A list $[a_i]$, $i=1, 2, \cdots, m$, the solution} + \KwOut{Processed list} + + $Solutions = [\ ]$ + + $firstSolution \leftarrow getRandomSolution()$ + + $Solutions.append(firstSolution)$ + + $lastSolution \leftarrow getLastElement(neighbour)$ + + $maxIterations \leftarrow 1000$ + + \For{$i \leftarrow 0$ \KwTo $maxIterations$}{ + \While{$neighbour \leq lastSolution$}{ + $neighbour \leftarrow getNeighbouringSolution(lastSolution)$ + + $Solutions.append(neighbour)$ + + $lastSolution \leftarrow getLastElement(neighbour)$ + } + $finalSolution \leftarrow getLastElement(Solutions)$ + } + \KwRet{$finalSolution$} +\end{algorithm} diff --git a/docs/Summary.pdf b/docs/Summary.pdf new file mode 100644 index 0000000000000000000000000000000000000000..51ff2b575f400d86ef31b9859d30a52447989e14 GIT binary patch literal 455914 zcmeFacU%))_b)m~Z=#|E1!*=E2t}nR%@RdX5m7-2MG!?=LXqAC6?IU+1|mhVpdu(r 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