{"id":7046,"date":"2012-01-26T23:43:32","date_gmt":"2012-01-26T21:43:32","guid":{"rendered":"http:\/\/hgpu.org\/?p=7046"},"modified":"2012-01-26T23:43:32","modified_gmt":"2012-01-26T21:43:32","slug":"parallel-particle-swarm-optimization-using-gpgpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=7046","title":{"rendered":"Parallel particle swarm optimization using GPGPU"},"content":{"rendered":"<p>This work presents a parallelization method for the Particle Swarm Optimization algorithm using a low-cost architecture: a General Purpose Graphics Processing Unit (GPGPU). The strategies to better suit the architecture main characteristics are addressed  along success rates and convergence times for the optimization of Rastrigin&#8217;s  and Ackley&#8217;s  functions on a 30-dimensional search space, and compared with results previously obtained using a cluster implementation.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This work presents a parallelization method for the Particle Swarm Optimization algorithm using a low-cost architecture: a General Purpose Graphics Processing Unit (GPGPU). The strategies to better suit the architecture main characteristics are addressed along success rates and convergence times for the optimization of Rastrigin&#8217;s and Ackley&#8217;s functions on a 30-dimensional search space, and compared [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,11,89,3],"tags":[1787,1782,14,613,20,1015,298],"class_list":["post-7046","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-computer-science","tag-cuda","tag-evolutionary-computations","tag-nvidia","tag-nvidia-geforce-gtx-460","tag-optimization"],"views":2310,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7046","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=7046"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7046\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7046"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7046"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7046"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}