{"id":"https://openalex.org/W2898616972","doi":"https://doi.org/10.1145/3274250.3274252","title":"Estimation of Three-Parameter Weibull Distribution Based on Artificial Fish-Swarm Algorithm","display_name":"Estimation of Three-Parameter Weibull Distribution Based on Artificial Fish-Swarm Algorithm","publication_year":2018,"publication_date":"2018-07-15","ids":{"openalex":"https://openalex.org/W2898616972","doi":"https://doi.org/10.1145/3274250.3274252","mag":"2898616972"},"language":"en","primary_location":{"id":"doi:10.1145/3274250.3274252","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3274250.3274252","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 1st International Conference on Mathematics and Statistics","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/A5054304572","display_name":"Xiangpo Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210163363","display_name":"PLA Army Engineering University","ror":"https://ror.org/05mgp8x93","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210163363"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiangpo Zhang","raw_affiliation_strings":["Field Engineering College, Army Engineering University of PLA, Nanjing China"],"affiliations":[{"raw_affiliation_string":"Field Engineering College, Army Engineering University of PLA, Nanjing China","institution_ids":["https://openalex.org/I4210163363"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5054304572"],"corresponding_institution_ids":["https://openalex.org/I4210163363"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.13003264,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"34","last_page":"40"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10780","display_name":"Reliability and Maintenance Optimization","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9901999831199646,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/weibull-distribution","display_name":"Weibull distribution","score":0.8384477496147156},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.7365880608558655},{"id":"https://openalex.org/keywords/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.7135589122772217},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5996202230453491},{"id":"https://openalex.org/keywords/swarm-behaviour","display_name":"Swarm behaviour","score":0.595896303653717},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.5894654989242554},{"id":"https://openalex.org/keywords/maximization","display_name":"Maximization","score":0.5823456048965454},{"id":"https://openalex.org/keywords/maximum-likelihood-sequence-estimation","display_name":"Maximum likelihood sequence estimation","score":0.5745115280151367},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.560335099697113},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5277586579322815},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.47112393379211426},{"id":"https://openalex.org/keywords/fish-actinopterygii","display_name":"Fish <Actinopterygii>","score":0.44706445932388306},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4184281527996063},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.38564804196357727},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.08349835872650146}],"concepts":[{"id":"https://openalex.org/C173291955","wikidata":"https://www.wikidata.org/wiki/Q732332","display_name":"Weibull distribution","level":2,"score":0.8384477496147156},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.7365880608558655},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.7135589122772217},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5996202230453491},{"id":"https://openalex.org/C181335050","wikidata":"https://www.wikidata.org/wiki/Q14915018","display_name":"Swarm behaviour","level":2,"score":0.595896303653717},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.5894654989242554},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.5823456048965454},{"id":"https://openalex.org/C191462741","wikidata":"https://www.wikidata.org/wiki/Q6795902","display_name":"Maximum likelihood sequence estimation","level":3,"score":0.5745115280151367},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.560335099697113},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5277586579322815},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.47112393379211426},{"id":"https://openalex.org/C2909208804","wikidata":"https://www.wikidata.org/wiki/Q127282","display_name":"Fish <Actinopterygii>","level":2,"score":0.44706445932388306},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4184281527996063},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38564804196357727},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.08349835872650146},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C505870484","wikidata":"https://www.wikidata.org/wiki/Q180538","display_name":"Fishery","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3274250.3274252","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3274250.3274252","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 1st International Conference on Mathematics and Statistics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"display_name":"Responsible consumption and production","id":"https://metadata.un.org/sdg/12"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320308142","display_name":"Texas Parks and Wildlife Department","ror":"https://ror.org/02b5k3s39"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W47577131","https://openalex.org/W1968781945","https://openalex.org/W1976004269","https://openalex.org/W2024028531","https://openalex.org/W2030414309","https://openalex.org/W2037715776","https://openalex.org/W2045001586","https://openalex.org/W2047951486","https://openalex.org/W2049307707","https://openalex.org/W2052325847","https://openalex.org/W2069837130","https://openalex.org/W2085546606","https://openalex.org/W2092534650","https://openalex.org/W2104037336","https://openalex.org/W2136538690","https://openalex.org/W2140862329","https://openalex.org/W2157276762","https://openalex.org/W2169748037","https://openalex.org/W2348551207","https://openalex.org/W2348677607","https://openalex.org/W2356147556","https://openalex.org/W2356895174","https://openalex.org/W2366849883","https://openalex.org/W2428472680","https://openalex.org/W4255746160","https://openalex.org/W6602366960"],"related_works":["https://openalex.org/W1978153144","https://openalex.org/W102848802","https://openalex.org/W1512911331","https://openalex.org/W1575258345","https://openalex.org/W2025556230","https://openalex.org/W2150616621","https://openalex.org/W2122958477","https://openalex.org/W2150061385","https://openalex.org/W2187551048","https://openalex.org/W2898616972"],"abstract_inverted_index":{"Three-parameter":[0],"Weibull":[1],"distribution":[2,14,154],"(TPWD)":[3],"plays":[4],"an":[5],"important":[6,27],"role":[7],"and":[8,28,123,132,142,152],"is":[9,43,70,82,118],"widely":[10],"used":[11],"in":[12,16,87,115],"failure":[13],"modeling":[15],"reliability":[17,151],"studies,":[18],"which":[19],"makes":[20],"the":[21,47,53,62,66,74,79,88,93,102,111,138,150],"estimation":[22,42,56],"of":[23,39,95,140,155],"its":[24],"parameters":[25,41,94,139],"very":[26],"a":[29,36,125,130,145],"hot":[30],"study":[31,108],"topic.":[32],"In":[33,59],"this":[34,116],"paper,":[35],"new":[37,112,131,146],"method":[38,113],"TPWD":[40,96],"proposed":[44,89,114],"by":[45,72],"integrating":[46],"artificial":[48],"fish-swarm":[49],"algorithm":[50],"(AFSA)":[51],"with":[52],"maximum":[54,67,75,103],"likelihood":[55,76,104],"(MLE)":[57],"method.":[58,90],"contrast":[60],"to":[61,101,120,136,148],"existing":[63],"methods,":[64],"where":[65],"log-likelihood":[68,80],"value":[69],"obtained":[71,99],"solving":[73],"equations":[77],"set,":[78],"maximization":[81],"achieved":[83],"directly":[84],"using":[85],"AFSA":[86],"And":[91],"then":[92],"can":[97],"be":[98,121],"according":[100],"value.":[105],"The":[106],"case":[107],"shows":[109],"that":[110],"paper":[117],"easy":[119],"processed":[122],"has":[124],"good":[126],"precision.":[127],"It":[128],"provides":[129,144],"highly":[133],"efficient":[134],"way":[135,147],"estimate":[137],"TPWD,":[141],"therefore":[143],"evaluate":[149],"life":[153,158],"products":[156],"whose":[157],"distributions":[159],"are":[160],"considered":[161],"as":[162],"typical":[163],"TPWD.":[164]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
