{"id":"https://openalex.org/W3092589772","doi":"https://doi.org/10.1080/01969722.2020.1827797","title":"Efficient Hyperparameter Optimization for Convolution Neural Networks in Deep Learning: A Distributed Particle Swarm Optimization Approach","display_name":"Efficient Hyperparameter Optimization for Convolution Neural Networks in Deep Learning: A Distributed Particle Swarm Optimization Approach","publication_year":2020,"publication_date":"2020-10-08","ids":{"openalex":"https://openalex.org/W3092589772","doi":"https://doi.org/10.1080/01969722.2020.1827797","mag":"3092589772"},"language":"en","primary_location":{"id":"doi:10.1080/01969722.2020.1827797","is_oa":false,"landing_page_url":"https://doi.org/10.1080/01969722.2020.1827797","pdf_url":null,"source":{"id":"https://openalex.org/S117436046","display_name":"Cybernetics & Systems","issn_l":"0196-9722","issn":["0196-9722","1087-6553"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cybernetics and Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101652862","display_name":"Yu Guo","orcid":"https://orcid.org/0000-0002-5489-8288"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Guo","raw_affiliation_strings":["School of Computer Science and Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100632977","display_name":"Jian-Yu Li","orcid":"https://orcid.org/0000-0002-6143-9207"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian-Yu Li","raw_affiliation_strings":["School of Computer Science and Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011145004","display_name":"Zhi\u2010Hui Zhan","orcid":"https://orcid.org/0000-0003-0862-0514"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhi-Hui Zhan","raw_affiliation_strings":["School of Computer Science and Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-0862-0514","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5011145004"],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":null,"apc_paid":null,"fwci":5.0792,"has_fulltext":false,"cited_by_count":84,"citation_normalized_percentile":{"value":0.96469585,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"52","issue":"1","first_page":"36","last_page":"57"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9944999814033508,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.9270190000534058},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7312880158424377},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.7174644470214844},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6747877597808838},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.6487702131271362},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6102309823036194},{"id":"https://openalex.org/keywords/multi-swarm-optimization","display_name":"Multi-swarm optimization","score":0.5036715865135193},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.45712679624557495},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45103415846824646},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44553354382514954},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4452645182609558},{"id":"https://openalex.org/keywords/swarm-behaviour","display_name":"Swarm behaviour","score":0.44417649507522583},{"id":"https://openalex.org/keywords/metaheuristic","display_name":"Metaheuristic","score":0.4164997339248657},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3225337266921997},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13268312811851501},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.08547705411911011}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.9270190000534058},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7312880158424377},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.7174644470214844},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6747877597808838},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.6487702131271362},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6102309823036194},{"id":"https://openalex.org/C122357587","wikidata":"https://www.wikidata.org/wiki/Q6934508","display_name":"Multi-swarm optimization","level":3,"score":0.5036715865135193},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45712679624557495},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45103415846824646},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44553354382514954},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4452645182609558},{"id":"https://openalex.org/C181335050","wikidata":"https://www.wikidata.org/wiki/Q14915018","display_name":"Swarm behaviour","level":2,"score":0.44417649507522583},{"id":"https://openalex.org/C109718341","wikidata":"https://www.wikidata.org/wiki/Q1385229","display_name":"Metaheuristic","level":2,"score":0.4164997339248657},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3225337266921997},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13268312811851501},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.08547705411911011}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/01969722.2020.1827797","is_oa":false,"landing_page_url":"https://doi.org/10.1080/01969722.2020.1827797","pdf_url":null,"source":{"id":"https://openalex.org/S117436046","display_name":"Cybernetics & Systems","issn_l":"0196-9722","issn":["0196-9722","1087-6553"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cybernetics and Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.4099999964237213}],"awards":[{"id":"https://openalex.org/G2632940600","display_name":"\u5206\u5e03\u5f0f\u5dee\u5206\u8fdb\u5316\u7b97\u6cd5\u6c42\u89e3\u5927\u89c4\u6a21\u52a8\u6001\u4f18\u5316\u95ee\u9898\u7814\u7a76","funder_award_id":"61772207","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5667035156","display_name":null,"funder_award_id":"2019YFB2102102","funder_id":"https://openalex.org/F4320321537","funder_display_name":"State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center"},{"id":"https://openalex.org/G7899725099","display_name":null,"funder_award_id":"61822602","funder_id":"https://openalex.org/F4320309856","funder_display_name":"National Youth Science Foundation"},{"id":"https://openalex.org/G835260656","display_name":"\u57fa\u4e8e\u8fdb\u5316\u8ba1\u7b97\u7684\u65b0\u95fb\u4f20\u64ad\u5e94\u5bf9\u7b56\u7565\u4f18\u5316\u7814\u7a76","funder_award_id":"61873097","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320309856","display_name":"National Youth Science Foundation","ror":"https://ror.org/054yz2f06"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321537","display_name":"State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center","ror":"https://ror.org/001ycj259"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W1595159159","https://openalex.org/W1616262590","https://openalex.org/W1831950995","https://openalex.org/W1881758074","https://openalex.org/W1992913497","https://openalex.org/W1994197834","https://openalex.org/W2030347721","https://openalex.org/W2097117768","https://openalex.org/W2100495367","https://openalex.org/W2112796928","https://openalex.org/W2119821739","https://openalex.org/W2122111042","https://openalex.org/W2147800946","https://openalex.org/W2152195021","https://openalex.org/W2154929945","https://openalex.org/W2156262512","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2257979135","https://openalex.org/W2344235023","https://openalex.org/W2489159973","https://openalex.org/W2829536470","https://openalex.org/W2895851783","https://openalex.org/W2902986194","https://openalex.org/W2905147046","https://openalex.org/W2914393402","https://openalex.org/W2919115771","https://openalex.org/W2938111495","https://openalex.org/W2947137917","https://openalex.org/W2963418938","https://openalex.org/W2963446712","https://openalex.org/W2963946985","https://openalex.org/W2973261831","https://openalex.org/W2974803302","https://openalex.org/W2977620944","https://openalex.org/W2979736100","https://openalex.org/W2980775892","https://openalex.org/W2981943969","https://openalex.org/W3003566741","https://openalex.org/W3012446350","https://openalex.org/W3013056622","https://openalex.org/W3034533053","https://openalex.org/W3048904406","https://openalex.org/W3102431071","https://openalex.org/W4236965008","https://openalex.org/W4239510810"],"related_works":["https://openalex.org/W2058965144","https://openalex.org/W2164382479","https://openalex.org/W2146343568","https://openalex.org/W98480971","https://openalex.org/W2150291671","https://openalex.org/W2013643406","https://openalex.org/W2161494499","https://openalex.org/W2911636622","https://openalex.org/W3135446416","https://openalex.org/W2778515884"],"abstract_inverted_index":{"Convolution":[0],"neural":[1],"network":[2,28,39,44],"(CNN)":[3],"is":[4,48],"a":[5,49,64,105,130],"kind":[6],"of":[7,32],"powerful":[8],"and":[9,34,84,97,108,166],"efficient":[10,43],"deep":[11],"learning":[12],"approach":[13,91,109,127,156],"that":[14,152],"has":[15,171],"obtained":[16],"great":[17],"success":[18],"in":[19],"many":[20],"real-world":[21],"applications.":[22],"However,":[23],"due":[24],"to":[25,76,81,99],"its":[26],"complex":[27],"structure,":[29],"the":[30,35,57,74,88,94,112,121,125,136,153,160,168,174],"intertwining":[31],"hyperparameters,":[33],"time-consuming":[36],"procedure":[37],"for":[38,46,110],"training,":[40],"finding":[41,111],"an":[42],"configuration":[45],"CNN":[47,161],"challenging":[50],"yet":[51],"tough":[52],"work.":[53],"To":[54],"efficiently":[55],"solve":[56],"hyperparameters":[58,75,95],"setting":[59],"problem,":[60],"this":[61],"paper":[62],"proposes":[63],"distributed":[65,122],"particle":[66,138],"swarm":[67,139],"optimization":[68,140],"(DPSO)":[69],"approach,":[70],"which":[71,103],"can":[72,92,128,157],"optimize":[73],"find":[77,159],"high-performing":[78],"CNNs.":[79],"Compared":[80],"tedious,":[82],"historical-experience-based,":[83],"personal-preference-based":[85],"manual":[86],"designs,":[87],"proposed":[89,154],"DPSO":[90,126,155],"evolve":[93],"automatically":[96],"globally":[98],"obtain":[100],"promising":[101,164],"CNNs,":[102],"provides":[104],"new":[106],"idea":[107],"global":[113],"optimal":[114],"hyperparameter":[115],"combination.":[116],"Moreover,":[117],"by":[118],"cooperating":[119],"with":[120,135,163,179],"computing":[123],"techniques,":[124],"have":[129,150],"considerable":[131],"speedup":[132],"when":[133,177],"compared":[134,178],"traditional":[137,180],"(PSO)":[141],"algorithm.":[142],"Extensive":[143],"experiments":[144],"on":[145],"widely-used":[146],"image":[147],"classification":[148],"benchmarks":[149],"verified":[151],"effectively":[158],"model":[162],"performance,":[165],"at":[167],"same":[169],"time,":[170],"greatly":[172],"reduced":[173],"computational":[175],"time":[176],"PSO.":[181]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":21},{"year":2022,"cited_by_count":23},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
