{"id":"https://openalex.org/W7128386546","doi":"https://doi.org/10.48550/arxiv.2602.06610","title":"Green Optimization: Energy-aware Design of Metaheuristics by Using Machine Learning Surrogates to Cope with Real Problems","display_name":"Green Optimization: Energy-aware Design of Metaheuristics by Using Machine Learning Surrogates to Cope with Real Problems","publication_year":2026,"publication_date":"2026-02-06","ids":{"openalex":"https://openalex.org/W7128386546","doi":"https://doi.org/10.48550/arxiv.2602.06610"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.06610","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.06610","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2602.06610","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041453383","display_name":"Tomohiro Harada","orcid":"https://orcid.org/0000-0002-0704-4351"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Harada, Tomohiro","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125393106","display_name":"Enrique Alba","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alba, Enrique","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5007483425","display_name":"Gabriel Luque","orcid":"https://orcid.org/0000-0001-7909-1416"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luque, Gabriel","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5041453383"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.8562999963760376,"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"}},"topics":[{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.8562999963760376,"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"}},{"id":"https://openalex.org/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.03539999946951866,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.019099999219179153,"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/metaheuristic","display_name":"Metaheuristic","score":0.7692000269889832},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.5644000172615051},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.4796000123023987},{"id":"https://openalex.org/keywords/retraining","display_name":"Retraining","score":0.47920000553131104},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4447000026702881},{"id":"https://openalex.org/keywords/surrogate-model","display_name":"Surrogate model","score":0.4162999987602234},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.3952000141143799},{"id":"https://openalex.org/keywords/machine-tool","display_name":"Machine tool","score":0.3578999936580658}],"concepts":[{"id":"https://openalex.org/C109718341","wikidata":"https://www.wikidata.org/wiki/Q1385229","display_name":"Metaheuristic","level":2,"score":0.7692000269889832},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.711899995803833},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6963000297546387},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6220999956130981},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.5644000172615051},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.4796000123023987},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.47920000553131104},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4447000026702881},{"id":"https://openalex.org/C131675550","wikidata":"https://www.wikidata.org/wiki/Q7646884","display_name":"Surrogate model","level":2,"score":0.4162999987602234},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.3952000141143799},{"id":"https://openalex.org/C5941749","wikidata":"https://www.wikidata.org/wiki/Q19768","display_name":"Machine tool","level":2,"score":0.3578999936580658},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31119999289512634},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C139143892","wikidata":"https://www.wikidata.org/wiki/Q7441615","display_name":"Search-based software engineering","level":5,"score":0.30149999260902405},{"id":"https://openalex.org/C14961307","wikidata":"https://www.wikidata.org/wiki/Q5377176","display_name":"Energy minimization","level":2,"score":0.2996000051498413},{"id":"https://openalex.org/C3019966295","wikidata":"https://www.wikidata.org/wiki/Q1341368","display_name":"Energy cost","level":2,"score":0.2879999876022339},{"id":"https://openalex.org/C13736549","wikidata":"https://www.wikidata.org/wiki/Q4489420","display_name":"Industrial engineering","level":1,"score":0.2858000099658966},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.28189998865127563},{"id":"https://openalex.org/C68781425","wikidata":"https://www.wikidata.org/wiki/Q2052203","display_name":"Multi-objective optimization","level":2,"score":0.28139999508857727},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.28119999170303345},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.28040000796318054},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2508000135421753},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.2508000135421753},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.06610","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.06610","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2602.06610","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.06610","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"score":0.9060401320457458,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Addressing":[0],"real-world":[1],"optimization":[2],"challenges":[3],"requires":[4],"not":[5],"only":[6],"advanced":[7],"metaheuristics":[8,33],"but":[9],"also":[10,158],"continuous":[11,135],"refinement":[12],"of":[13,22,28,50,81],"their":[14,54],"internal":[15],"mechanisms.":[16],"This":[17],"paper":[18],"explores":[19],"the":[20,26,47,79,117,127],"integration":[21],"machine":[23],"learning":[24],"in":[25],"form":[27],"neural":[29],"surrogate":[30,95],"models":[31],"into":[32,73,193],"through":[34],"a":[35,70,92,160,179],"recent":[36],"lens:":[37],"energy":[38,58,98,187],"consumption.":[39],"While":[40],"surrogates":[41],"are":[42],"widely":[43],"used":[44],"to":[45,77,102,183],"reduce":[46,97],"computational":[48,129],"cost":[49,153],"expensive":[51],"objective":[52],"functions,":[53],"combined":[55],"impact":[56,162],"on":[57,143,163],"efficiency,":[59],"algorithmic":[60],"performance,":[61],"and":[62,109,151,165,169,190],"solution":[63],"accuracy":[64,150,166,192],"remains":[65],"largely":[66],"unquantified.":[67],"We":[68],"provide":[69],"critical":[71],"investigation":[72],"this":[74],"intersection,":[75],"aiming":[76],"advance":[78],"design":[80],"energy-aware,":[82],"surrogate-assisted":[83,184],"search":[84],"algorithms.":[85],"Our":[86],"experiments":[87],"reveal":[88],"substantial":[89],"benefits:":[90],"employing":[91],"state-of-the-art":[93],"pre-trained":[94],"can":[96],"consumption":[99],"by":[100,106,112,125],"up":[101],"98\\%,":[103],"execution":[104],"time":[105,189],"approximately":[107],"98%,":[108],"memory":[110],"usage":[111],"around":[113],"99\\%.":[114],"Moreover,":[115],"increasing":[116],"training":[118],"dataset":[119],"size":[120],"further":[121],"enhances":[122],"these":[123],"gains":[124],"lowering":[126],"per-use":[128],"cost,":[130],"while":[131],"static":[132],"pre-training":[133],"versus":[134],"(iterative)":[136],"retraining":[137],"have":[138,159],"relatively":[139],"different":[140],"advantages":[141],"depending":[142],"whether":[144],"we":[145],"aim":[146],"at":[147,167],"time/energy":[148],"or":[149],"general":[152],"across":[154],"problems,":[155],"respectively.":[156],"Surrogates":[157],"negative":[161],"costs":[164],"times,":[168],"then":[170],"they":[171],"cannot":[172],"be":[173],"blindly":[174],"adopted.":[175],"These":[176],"findings":[177],"support":[178],"more":[180],"holistic":[181],"approach":[182],"optimization,":[185],"integrating":[186],"with":[188],"predictive":[191],"performance":[194],"assessments.":[195]},"counts_by_year":[],"updated_date":"2026-02-10T06:17:13.238206","created_date":"2026-02-10T00:00:00"}
