{"id":"W2106389767","doi":"10.1115/ipack2007-33256","title":"A Modified Particle Swarm Optimization Scheme and Its Application in Electronic Heat Sink Design","year":2007,"lang":"en","type":"article","venue":"","topic":"Heat Transfer and Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Particle swarm optimization; Mathematical optimization; Heat sink; Benchmark (surveying); Computer science; Chaotic; Entropy (arrow of time); Multi-swarm optimization; Metaheuristic; Evolutionary computation; Algorithm; Mathematics; Engineering; Artificial intelligence; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002494991,0.00008146489,0.00007545469,0.0000592743,0.0000249087,0.00001496437,0.00003283996,0.00006633915,0.00001139377],"category_scores_gemma":[0.000006390238,0.00008618744,0.00000937187,0.0002624025,0.000005519837,0.0001610226,0.00000327229,0.00007370538,0.000006541083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007381474,"about_ca_system_score_gemma":0.00001081176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001271849,"about_ca_topic_score_gemma":0.00004573602,"domain_scores_codex":[0.9993842,0.00001162088,0.0001645383,0.000119997,0.00006467934,0.0002550235],"domain_scores_gemma":[0.9998258,0.00002869735,0.000002589662,0.00006911577,0.00002026034,0.00005350129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001443901,0.00001557389,0.0001256901,0.00001241113,0.000003571552,3.667446e-7,0.0001052654,0.9807409,0.01550542,0.002418334,0.000003963659,0.001054054],"study_design_scores_gemma":[0.0003751271,0.0000196447,0.0002791274,0.000004006758,0.000003437506,0.000001423433,0.00001261905,0.9207233,0.07840805,0.00006480405,0.00001650696,0.00009200601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1512971,0.0002576697,0.8470865,0.00004234865,0.00001161566,0.0002811507,2.56052e-7,0.0001736171,0.000849713],"genre_scores_gemma":[0.9895503,0.0002473386,0.01008383,0.00003640336,0.0000150499,0.00002651966,0.000008278722,0.00001776062,0.00001451644],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8382531,"threshold_uncertainty_score":0.3514622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0138171192808107,"score_gpt":0.2217190310506738,"score_spread":0.2079019117698631,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}