{"id":1353,"date":"2010-11-09T11:30:21","date_gmt":"2010-11-09T11:30:21","guid":{"rendered":"http:\/\/hgpu.org\/?p=1353"},"modified":"2010-11-09T11:30:21","modified_gmt":"2010-11-09T11:30:21","slug":"nonlinear-optimization-with-a-massively-parallel-evolution-strategy-pattern-search-algorithm-on-graphics-hardware","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=1353","title":{"rendered":"Nonlinear optimization with a massively parallel Evolution Strategy-Pattern Search algorithm on graphics hardware"},"content":{"rendered":"<p>This paper presents a massively parallel Evolution Strategy-Pattern Search Optimization (ES-PS) algorithm with graphics hardware acceleration on bound constrained nonlinear continuous optimization problems. The algorithm was specifically designed for a graphic processing unit (GPU) hardware platform featuring &#8216;Single Instruction Multiple Thread&#8217; (SIMT). Evolution Strategy is a population-based evolutionary algorithm for solving complex optimization problems. GPU computing is an emerging desktop parallel computing platform. The hybrid ES-PS optimization method was implemented in the GPU environment and compared to a similar implementation on Central Processing Units (CPU). Computational results indicated that GPU-accelerated SIMT-ES-PS method was orders of magnitude faster than the corresponding CPU implementation. The main contribution of this paper was the parallelization analysis and performance analysis of the hybrid ES-PS with GPU acceleration. The computational results demonstrated a promising direction for high speed optimization with desktop parallel computing on a personal computer (PC).<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents a massively parallel Evolution Strategy-Pattern Search Optimization (ES-PS) algorithm with graphics hardware acceleration on bound constrained nonlinear continuous optimization problems. The algorithm was specifically designed for a graphic processing unit (GPU) hardware platform featuring &#8216;Single Instruction Multiple Thread&#8217; (SIMT). Evolution Strategy is a population-based evolutionary algorithm for solving complex optimization problems. GPU [&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":[11,3],"tags":[1782,613,615],"class_list":["post-1353","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-computer-science","tag-evolutionary-computations","tag-pattern-search"],"views":1805,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1353","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=1353"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1353\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1353"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1353"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1353"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}