{"id":1184,"date":"2019-11-25T15:21:08","date_gmt":"2019-11-25T17:21:08","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=1184"},"modified":"2019-11-25T15:21:08","modified_gmt":"2019-11-25T17:21:08","slug":"vol17-no2-art4","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol17-no2\/vol17-no2-art4\/","title":{"rendered":"Hybrid differential evolution with the topographical heuristic"},"content":{"rendered":"<p><strong><font size=\"+2\">Almoaia, A.E.N.F. <a href=\"http:\/\/orcid.org\/0000-0003-2518-7353\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2019\/11\/orcid-2.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>, Sacco, W.F.\u00a0<a href=\"http:\/\/orcid.org\/0000-0002-8358-0850\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2019\/11\/orcid-2.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>, Silva Neto, A.J.\u00a0<a href=\"http:\/\/orcid.org\/0000-0002-9616-6093\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2019\/11\/orcid-2.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a><\/font><\/strong><\/p>\n<p align=\"justify\"><strong>Abstract:<\/strong> In this article, we present a new hybrid differential evolution (DE) which employs a topographical heuristic introduced in the early nineties as part of a global optimization method. This heuristic is used to select individuals from the DE population in order to be starting points of instances of the Hooke\u2013Jeeves algorithm. The solutions achieved in this phase are potential candidates for the next generation. The method, called TopoDE, is compared with other stochastic optimization algorithms using challenging benchmark problems. The results obtained are quite promising.<\/p>\n<p><strong>Keywords:<\/strong> Differential Evolution, Topographical Heuristic, Hybrid methods, Hooke\u2013Jeeves method.<\/p>\n<p><strong>DOI code:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/lnlm-vol17-no2-art4\">10.21528\/lnlm-vol17-no2-art4<\/a><\/p>\n<p><strong>PDF file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2019\/11\/vol17-no2-art4.pdf\" rel=\"\">vol17-no2-art4.pdf<\/a><\/p>\n<p><strong>BibTex file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2019\/11\/vol17-no2-art4.bib\" rel=\"\">vol17-no2-art4.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Almoaia, A.E.N.F. , Sacco, W.F.\u00a0, Silva Neto, A.J.\u00a0 Abstract: In this article, we present a new hybrid differential evolution (DE) which employs a topographical heuristic introduced in the early nineties as part of a global optimization method. This heuristic is <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol17-no2\/vol17-no2-art4\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":1149,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1184","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Hybrid differential evolution with the topographical heuristic - Learning and NonLinear Models<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol17-no2\/vol17-no2-art4\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Hybrid differential evolution with the topographical heuristic - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"Almoaia, A.E.N.F. , Sacco, W.F.\u00a0, Silva Neto, A.J.\u00a0 Abstract: In this article, we present a new hybrid differential evolution (DE) which employs a topographical heuristic introduced in the early nineties as part of a global optimization method. 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