{"id":3048,"date":"2016-05-20T15:55:25","date_gmt":"2016-05-20T14:55:25","guid":{"rendered":"http:\/\/smart--grid.net\/?page_id=3048"},"modified":"2022-12-03T22:59:02","modified_gmt":"2022-12-03T21:59:02","slug":"grasp","status":"publish","type":"page","link":"https:\/\/complex-systems-ai.com\/es\/algoritmos-estocasticos\/sujetar\/","title":{"rendered":"SUJETAR"},"content":{"rendered":"<div data-elementor-type=\"wp-page\" data-elementor-id=\"3048\" class=\"elementor elementor-3048\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-624d9e5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"624d9e5\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-28bbd90\" data-id=\"28bbd90\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7c5bd5c elementor-align-justify elementor-widget elementor-widget-button\" data-id=\"7c5bd5c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/complex-systems-ai.com\/es\/algoritmos-estocasticos\/\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Algoritmos estoc\u00e1sticos<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-66b35f6\" data-id=\"66b35f6\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-05a2a63 elementor-align-justify elementor-widget elementor-widget-button\" data-id=\"05a2a63\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/complex-systems-ai.com\/es\/\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Pagina de inicio<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-ffd98b6\" data-id=\"ffd98b6\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f00d751 elementor-align-justify elementor-widget elementor-widget-button\" data-id=\"f00d751\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/fr.wikipedia.org\/wiki\/Greedy_randomized_adaptive_search_procedure\" target=\"_blank\" rel=\"noopener\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Wiki<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-306e4b57 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"306e4b57\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-774b4010\" data-id=\"774b4010\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1cd16116 elementor-widget elementor-widget-text-editor\" data-id=\"1cd16116\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Contenido<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Tabla de contenido alternativo\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Palanca<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewbox=\"0 0 24 24\" version=\"1.2\" baseprofile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/complex-systems-ai.com\/es\/algoritmos-estocasticos\/sujetar\/#Greedy-Randomized-Adaptive-Search-Procedure-GRASP\" >Procedimiento de b\u00fasqueda adaptativa aleatoria codiciosa (GRASP)<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Greedy-Randomized-Adaptive-Search-Procedure-GRASP\"><\/span>Procedimiento de b\u00fasqueda adaptativa aleatoria codiciosa (GRASP)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>El algoritmo Greedy Randomized Adaptive Search Procedure (GRASP) es un <a href=\"https:\/\/complex-systems-ai.com\/es\/optimizacion-combinatoria\/\">metaheur\u00edstica<\/a> introducido por Feo y Resende en 1989.<\/p>\n\n<div style=\"padding: 5px; background-color: #d5edff; border: 2px solid #3c95e8; -moz-border-radius: 9px; -khtml-border-radius: 9px; -webkit-border-radius: 9px; border-radius: 9px;\">Su funcionamiento se basa en la repetici\u00f3n de dos fases: una construcci\u00f3n glotona seguida de una <a href=\"https:\/\/complex-systems-ai.com\/es\/algoritmos-estocasticos\/metodos-de-descenso\/\">busqueda local<\/a>.<\/div>\n\n<p>La caracter\u00edstica del m\u00e9todo GRASP es su fase de construcci\u00f3n de una soluci\u00f3n. Para ello, el algoritmo mantiene una lista actualizada de fragmentos de posibles soluciones (RCL, lista de candidatos restringidos). La soluci\u00f3n se construye paso a paso yendo a elegir elementos (en nuestro caso, son las ganancias de combinar mallas en zonas) en la lista RCL. Esta lista est\u00e1 ordenada, es la parte codiciosa del algoritmo.<\/p>\n\n<p>Un elemento se extrae aleatoriamente de las mejores posibilidades de la lista RCL, es la parte aleatoria del algoritmo. Gracias a la parte aleatoria, la fase de construcci\u00f3n permite por tanto variar la forma de las soluciones generadas, pero estas son de buena calidad ya que la elecci\u00f3n aleatoria se realiza entre un conjunto de buenos candidatos. La investigaci\u00f3n local se aplica a la soluci\u00f3n factible resultante de la fase de construcci\u00f3n para ver si a\u00fan es posible mejorar esta soluci\u00f3n.<\/p>\n\n<div style=\"text-align: justify;\">Cabe se\u00f1alar dos puntos:<\/div>\n\n<ul class=\"wp-block-list\">\n<li style=\"text-align: justify;\">el RCL se actualiza con elementos seleccionados seg\u00fan una heur\u00edstica espec\u00edfica adaptada al problema considerado.<\/li>\n<li style=\"text-align: justify;\">la elecci\u00f3n de un elemento en el RCL para construir la soluci\u00f3n es aleatoria.<\/li>\n<\/ul>\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img fetchpriority=\"high\" decoding=\"async\" class=\"aligncenter wp-image-3949\" src=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2016\/05\/grasp.png\" alt=\"Procedimiento de b\u00fasqueda adaptativa aleatoria codiciosa GRASP\" width=\"722\" height=\"755\" title=\"\" srcset=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2016\/05\/grasp.png 722w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2016\/05\/grasp-287x300.png 287w\" sizes=\"(max-width: 722px) 100vw, 722px\" \/><\/figure>\n<\/div>\n\n<p>\u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"<p>Stochastic Algorithms Homepage Wiki Greedy Randomized Adaptive Search Procedure (GRASP) El algoritmo Greedy Randomized Adaptive Search Procedure (GRASP) es una metaheur\u00edstica introducida por Feo y... <\/p>","protected":false},"author":1,"featured_media":0,"parent":7101,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-3048","page","type-page","status-publish","hentry"],"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/pages\/3048","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/comments?post=3048"}],"version-history":[{"count":5,"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/pages\/3048\/revisions"}],"predecessor-version":[{"id":18433,"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/pages\/3048\/revisions\/18433"}],"up":[{"embeddable":true,"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/pages\/7101"}],"wp:attachment":[{"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/media?parent=3048"}],"curies":[{"name":"gracias","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}