{"id":"https://openalex.org/W2897979086","doi":"https://doi.org/10.1109/cec.2018.8477992","title":"A Discrete Fireworks Algorithm for Solving Large-Scale Travel Salesman Problem","display_name":"A Discrete Fireworks Algorithm for Solving Large-Scale Travel Salesman Problem","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2897979086","doi":"https://doi.org/10.1109/cec.2018.8477992","mag":"2897979086"},"language":"en","primary_location":{"id":"doi:10.1109/cec.2018.8477992","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec.2018.8477992","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 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/A5101634507","display_name":"Haoran Luo","orcid":"https://orcid.org/0000-0002-1951-6678"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoran Luo","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103257775","display_name":"Weidi Xu","orcid":"https://orcid.org/0000-0002-7279-9339"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weidi Xu","raw_affiliation_strings":["Key Laboratory of Machine Perception (MOE), Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Machine Perception (MOE), Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023089209","display_name":"Ying Tan","orcid":"https://orcid.org/0000-0001-8243-4731"},"institutions":[{"id":"https://openalex.org/I135598925","display_name":"Kyushu University","ror":"https://ror.org/00p4k0j84","country_code":"JP","type":"education","lineage":["https://openalex.org/I135598925"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ying Tan","raw_affiliation_strings":["Faculty of Design, Kyushu University, Fukuoka, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Design, Kyushu University, Fukuoka, Japan","institution_ids":["https://openalex.org/I135598925"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.5206,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.87330517,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9998999834060669,"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":0.9998999834060669,"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/T10567","display_name":"Vehicle Routing Optimization Methods","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/travelling-salesman-problem","display_name":"Travelling salesman problem","score":0.7710166573524475},{"id":"https://openalex.org/keywords/fireworks","display_name":"Fireworks","score":0.7296879887580872},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6330115795135498},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.614923894405365},{"id":"https://openalex.org/keywords/ant-colony-optimization-algorithms","display_name":"Ant colony optimization algorithms","score":0.6088656783103943},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.5550166964530945},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.5240885615348816},{"id":"https://openalex.org/keywords/metaheuristic","display_name":"Metaheuristic","score":0.5187604427337646},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5150432586669922},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.4782751500606537},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.4329968988895416},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.21479645371437073},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2068687081336975}],"concepts":[{"id":"https://openalex.org/C175859090","wikidata":"https://www.wikidata.org/wiki/Q322212","display_name":"Travelling salesman problem","level":2,"score":0.7710166573524475},{"id":"https://openalex.org/C2778758847","wikidata":"https://www.wikidata.org/wiki/Q127933","display_name":"Fireworks","level":2,"score":0.7296879887580872},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6330115795135498},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.614923894405365},{"id":"https://openalex.org/C40128228","wikidata":"https://www.wikidata.org/wiki/Q460851","display_name":"Ant colony optimization algorithms","level":2,"score":0.6088656783103943},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.5550166964530945},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.5240885615348816},{"id":"https://openalex.org/C109718341","wikidata":"https://www.wikidata.org/wiki/Q1385229","display_name":"Metaheuristic","level":2,"score":0.5187604427337646},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5150432586669922},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4782751500606537},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.4329968988895416},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.21479645371437073},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2068687081336975},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cec.2018.8477992","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec.2018.8477992","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Congress on Evolutionary Computation (CEC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W112498084","https://openalex.org/W744623092","https://openalex.org/W1482722110","https://openalex.org/W1535723536","https://openalex.org/W1905425803","https://openalex.org/W1968594877","https://openalex.org/W1991093619","https://openalex.org/W2001410023","https://openalex.org/W2005166868","https://openalex.org/W2008656089","https://openalex.org/W2009803313","https://openalex.org/W2025449594","https://openalex.org/W2042986967","https://openalex.org/W2055569927","https://openalex.org/W2078264277","https://openalex.org/W2107631366","https://openalex.org/W2154929945","https://openalex.org/W2163428398","https://openalex.org/W2165142526","https://openalex.org/W2343205087","https://openalex.org/W2488912457","https://openalex.org/W2496983863","https://openalex.org/W2502374279","https://openalex.org/W2513970330","https://openalex.org/W2559225686","https://openalex.org/W2586424584","https://openalex.org/W2750534491","https://openalex.org/W2798467024","https://openalex.org/W4232342035","https://openalex.org/W4302577885","https://openalex.org/W6628973536","https://openalex.org/W6722502305","https://openalex.org/W6724166371"],"related_works":["https://openalex.org/W1611875833","https://openalex.org/W4200602616","https://openalex.org/W3082258531","https://openalex.org/W2126939445","https://openalex.org/W2160072443","https://openalex.org/W2769848411","https://openalex.org/W1562604685","https://openalex.org/W2625683255","https://openalex.org/W2580862641","https://openalex.org/W4313485422"],"abstract_inverted_index":{"Fireworks":[0],"algorithm":[1,57,158,168,178],"(FWA)":[2],"is":[3,98,112,133],"a":[4,47,54,123],"newly":[5],"proposed":[6],"swarm":[7],"intelligence":[8],"optimization":[9,33,50],"method.":[10],"It":[11],"simulates":[12],"the":[13,20,42,62,72,89,102,117,129,154],"fireworks":[14,56,132,139],"explosion":[15,91,103],"process":[16],"to":[17,41,100,115,135,152],"search":[18],"for":[19,58,70,163],"best":[21],"location":[22],"of":[23,65,119,131,156],"sparks":[24],"and":[25,67,81,122,146,169],"has":[26],"demonstrated":[27],"good":[28,138],"performance":[29,155],"in":[30,93],"many":[31],"continuous":[32],"problems.":[34],"In":[35,78],"this":[36],"paper,":[37],"we":[38],"apply":[39],"FWA":[40,66],"travel":[43],"salesman":[44],"problem":[45],"(TSP),":[46],"classical":[48],"discrete":[49,55],"problem.":[51],"We":[52,74,174],"propose":[53],"TSP":[59],"by":[60],"combining":[61],"general":[63],"framework":[64],"current":[68,160],"ideas":[69],"solving":[71],"TSP.":[73],"call":[75],"it":[76],"DFWA-TSP.":[77],"DFWA-TSP,":[79],"2-opt":[80],"3-opt":[82],"edge":[83,120],"exchange":[84,121],"heuristic":[85,161],"are":[86],"implemented":[87],"as":[88,166],"basic":[90],"operation":[92],"FWA.":[94],"An":[95],"adaptive":[96],"strategy":[97],"designed":[99],"decide":[101],"amplitude.":[104],"A":[105],"particular":[106],"mutation":[107],"method":[108,126],"based":[109,127],"on":[110,128,143,183],"insertion":[111],"also":[113],"used":[114],"cover":[116],"shortage":[118],"new":[124],"selection":[125],"quality":[130],"adopted":[134],"pick":[136],"up":[137],"efficiently.":[140],"Various":[141],"experiments":[142],"both":[144],"TSPLIB":[145],"synthetic":[147],"data":[148],"have":[149],"been":[150],"made":[151],"compare":[153],"our":[157,177],"with":[159],"methods":[162],"TSP,":[164],"such":[165],"genetic":[167],"ant":[170],"colony":[171],"system":[172],"algorithm.":[173],"conclude":[175],"that":[176],"out-performs":[179],"these":[180],"algorithms,":[181],"especially":[182],"large-scale":[184],"cases.":[185]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}