{"id":"W2023026651","doi":"10.1145/2576768.2598280","title":"Identifying and exploiting the scale of a search space in particle swarm optimization","year":2014,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Attraction; Particle swarm optimization; Convergence (economics); Local optimum; Context (archaeology); Cluster analysis; Mathematical optimization; Computer science; Swarm behaviour; Range (aeronautics); Modal; Scale (ratio); Task (project management); Metaheuristic; Local search (optimization); Exploit; Multi-swarm optimization; Local convergence; Mathematics; Artificial intelligence; Geography; Engineering; Iterative method","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":[0.00157658,0.00005465671,0.0000974438,0.00008859501,0.00008028804,0.0001553219,0.0003620482,0.00002064493,0.0000287992],"category_scores_gemma":[0.000270618,0.00004009752,0.00001403327,0.0006332865,0.00006819262,0.0003147612,0.0003148377,0.0000906446,0.000006579116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001278189,"about_ca_system_score_gemma":0.0000248849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009940381,"about_ca_topic_score_gemma":0.0000150736,"domain_scores_codex":[0.9987995,0.0002456551,0.0002050991,0.0002061618,0.0003451047,0.0001984576],"domain_scores_gemma":[0.9991724,0.0003087179,0.0000414257,0.0003076459,0.0001127833,0.00005695528],"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":[0.000009314775,0.0001219154,0.01308898,0.0000863297,0.00001151354,0.000003054893,0.007145051,0.8017786,0.00178292,0.1238695,0.00006419628,0.05203859],"study_design_scores_gemma":[0.0002172055,0.00001775773,0.00175425,0.0000129796,9.438285e-7,0.000002469922,0.0002805541,0.9878494,0.009380703,0.0004279542,0.00001052036,0.00004531565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05263458,0.00002807242,0.9444352,0.0012066,0.00002949083,0.0001310869,1.380888e-7,0.00002780777,0.001507045],"genre_scores_gemma":[0.6686705,0.00002004604,0.3310446,0.00003660894,0.00001061073,0.000007202648,2.593801e-7,0.000004114982,0.0002060884],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6160359,"threshold_uncertainty_score":0.1635129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03700820844062096,"score_gpt":0.2969969364756348,"score_spread":0.2599887280350138,"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."}}