{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001693201,0.0005097094,0.000625776,0.0009397505,0.0005306261,0.00100402,0.0007353577,0.0009420656,0.0006027923],"category_scores_gemma":[0.006734035,0.0004055142,0.000568367,0.0006436093,0.001134279,0.001499853,0.001514472,0.0009858969,0.0001504954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005095352,"about_ca_system_score_gemma":0.0005700592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002282663,"about_ca_topic_score_gemma":0.001816518,"domain_scores_codex":[0.9994881,0.0001971017,0.00003034691,0.00007701437,0.0001724748,0.00003480203],"domain_scores_gemma":[0.9980571,0.001289182,0.0001944976,0.0002084661,0.0001895228,0.00006114828],"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":[0.00008585213,0.00004971015,0.002583404,0.0001636973,0.00007323347,0.0001763982,0.0002647644,0.8616942,0.0108089,0.03246008,0.0008252847,0.09081438],"study_design_scores_gemma":[0.000007618279,0.00002573159,0.000560061,0.0000110852,0.000006720838,0.00002877577,0.00002409507,0.9902647,0.000845041,0.007795406,0.0004176749,0.00001323842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02825661,0.000553564,0.968447,0.0001907684,0.00002698837,0.00005252444,0.00001732281,0.0001951189,0.00226025],"genre_scores_gemma":[0.6888594,0.0004514831,0.3093519,0.00007483946,0.00005943488,0.0001615719,0.00003994392,0.00007965479,0.0009217393],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002282663,"threshold_uncertainty_score":0.008954644,"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."}}