{"id":"https://openalex.org/W2081315321","doi":"https://doi.org/10.1109/cec.2014.6900439","title":"Particle swarm convergence: An empirical investigation","display_name":"Particle swarm convergence: An empirical investigation","publication_year":2014,"publication_date":"2014-07-01","ids":{"openalex":"https://openalex.org/W2081315321","doi":"https://doi.org/10.1109/cec.2014.6900439","mag":"2081315321"},"language":"en","primary_location":{"id":"doi:10.1109/cec.2014.6900439","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec.2014.6900439","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Congress on Evolutionary Computation (CEC)","raw_type":"proceedings-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/A5063774442","display_name":"Christopher W. Cleghorn","orcid":"https://orcid.org/0000-0002-7860-0650"},"institutions":[{"id":"https://openalex.org/I1340210623","display_name":"Volvo (Sweden)","ror":"https://ror.org/05b6ypc36","country_code":"SE","type":"company","lineage":["https://openalex.org/I1340210623"]},{"id":"https://openalex.org/I4387153628","display_name":"Volvo Cars (Sweden)","ror":"https://ror.org/005n5zv88","country_code":null,"type":"company","lineage":["https://openalex.org/I4210153393","https://openalex.org/I4387153628"]}],"countries":["SE"],"is_corresponding":true,"raw_author_name":"Christopher W Cleghorn","raw_affiliation_strings":["Dept. 94313/PVD1:2, VOLVO CAR CORPORATION, Gothenburg, Sweden","[Dept. 94313/PVD1:2, VOLVO CAR CORPORATION, Gothenburg, Sweden]"],"affiliations":[{"raw_affiliation_string":"Dept. 94313/PVD1:2, VOLVO CAR CORPORATION, Gothenburg, Sweden","institution_ids":["https://openalex.org/I1340210623","https://openalex.org/I4387153628"]},{"raw_affiliation_string":"[Dept. 94313/PVD1:2, VOLVO CAR CORPORATION, Gothenburg, Sweden]","institution_ids":["https://openalex.org/I1340210623","https://openalex.org/I4387153628"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039197997","display_name":"Andries P. Engelbrecht","orcid":"https://orcid.org/0000-0002-0242-3539"},"institutions":[{"id":"https://openalex.org/I66862912","display_name":"Chalmers University of Technology","ror":"https://ror.org/040wg7k59","country_code":"SE","type":"education","lineage":["https://openalex.org/I66862912"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Andries P Engelbrecht","raw_affiliation_strings":["CHALMERS UNIVERSITY OF TECHNOLOGY, Gothenburg, Sweden","Chalmers Univ., of Technol., Gothenburg, Sweden"],"affiliations":[{"raw_affiliation_string":"CHALMERS UNIVERSITY OF TECHNOLOGY, Gothenburg, Sweden","institution_ids":["https://openalex.org/I66862912"]},{"raw_affiliation_string":"Chalmers Univ., of Technol., Gothenburg, Sweden","institution_ids":["https://openalex.org/I66862912"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5063774442"],"corresponding_institution_ids":["https://openalex.org/I1340210623","https://openalex.org/I4387153628"],"apc_list":null,"apc_paid":null,"fwci":9.4073,"has_fulltext":false,"cited_by_count":61,"citation_normalized_percentile":{"value":0.98008747,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2524","last_page":"2530"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9965999722480774,"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"}},"topics":[{"id":"https://openalex.org/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9965999722480774,"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/T13187","display_name":"Diffusion and Search Dynamics","score":0.9907000064849854,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10482","display_name":"Mathematical and Theoretical Epidemiology and Ecology Models","score":0.9843999743461609,"subfield":{"id":"https://openalex.org/subfields/2739","display_name":"Public Health, Environmental and Occupational Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.7374675273895264},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.6745918393135071},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5451776385307312},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.5139355063438416},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5036439299583435},{"id":"https://openalex.org/keywords/particle","display_name":"Particle (ecology)","score":0.47917500138282776},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.44358912110328674},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.39805036783218384},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3678191900253296},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.32339054346084595},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.21455654501914978},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.12544280290603638},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.09270250797271729},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.08384519815444946}],"concepts":[{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.7374675273895264},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.6745918393135071},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5451776385307312},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.5139355063438416},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5036439299583435},{"id":"https://openalex.org/C2778517922","wikidata":"https://www.wikidata.org/wiki/Q7140482","display_name":"Particle (ecology)","level":2,"score":0.47917500138282776},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.44358912110328674},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.39805036783218384},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3678191900253296},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.32339054346084595},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.21455654501914978},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.12544280290603638},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.09270250797271729},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.08384519815444946},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cec.2014.6900439","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec.2014.6900439","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Congress on Evolutionary Computation (CEC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5099999904632568,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W129833132","https://openalex.org/W638950400","https://openalex.org/W1628481780","https://openalex.org/W1913619579","https://openalex.org/W1974730853","https://openalex.org/W1994445384","https://openalex.org/W2000884773","https://openalex.org/W2012939405","https://openalex.org/W2012986677","https://openalex.org/W2016695533","https://openalex.org/W2031225958","https://openalex.org/W2081749411","https://openalex.org/W2111393363","https://openalex.org/W2117595234","https://openalex.org/W2135232426","https://openalex.org/W2138273409","https://openalex.org/W2152195021","https://openalex.org/W2162012556","https://openalex.org/W2165299997","https://openalex.org/W2169245194","https://openalex.org/W2169291847","https://openalex.org/W6605188440","https://openalex.org/W6680800046"],"related_works":["https://openalex.org/W3019402777","https://openalex.org/W2031835531","https://openalex.org/W2388590088","https://openalex.org/W4386596916","https://openalex.org/W2097519318","https://openalex.org/W1969166468","https://openalex.org/W2116377213","https://openalex.org/W2122371747","https://openalex.org/W2378894046","https://openalex.org/W2022727000"],"abstract_inverted_index":{"This":[0],"paper":[1],"performs":[2],"a":[3,25,56,77,108],"thorough":[4],"empirical":[5,50,90],"investigation":[6],"of":[7,28,83,101],"the":[8,66,84,89,99,102,125],"conditions":[9],"placed":[10],"on":[11],"particle":[12,36],"swarm":[13],"optimization":[14],"control":[15],"parameters":[16,97],"to":[17,42,63],"ensure":[18,35],"convergent":[19,136],"behavior.":[20],"At":[21],"present":[22],"there":[23,75],"exists":[24],"large":[26],"number":[27],"theoretically":[29,68,85,103],"derived":[30,69,86,104],"parameter":[31,127,137],"regions":[32,87],"that":[33,61,74,96,122],"will":[34],"convergence,":[37],"however,":[38],"selecting":[39],"which":[40],"region":[41,57,105],"utilize":[43],"in":[44,123],"practice":[45],"is":[46,52,76,119],"not":[47,130],"obvious.":[48],"The":[49],"study":[51],"carried":[53],"out":[54],"over":[55],"slightly":[58],"larger":[59],"than":[60],"needed":[62],"contain":[64],"all":[65],"relevant":[67],"regions.":[70],"It":[71,92],"was":[72,93],"found":[73,95],"very":[78,109],"strong":[79],"correlation":[80],"between":[81],"one":[82],"and":[88],"evidence.":[91],"also":[94],"near":[98],"edge":[100,126],"converge":[106],"at":[107],"slow":[110],"rate,":[111],"after":[112],"an":[113],"initial":[114],"population":[115],"explosion.":[116],"Particle":[117],"convergence":[118],"so":[120],"slow,":[121],"practice,":[124],"settings":[128],"should":[129],"really":[131],"be":[132],"considered":[133],"useful":[134],"as":[135],"settings.":[138]},"counts_by_year":[{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":9},{"year":2016,"cited_by_count":8},{"year":2015,"cited_by_count":5},{"year":2014,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}