{"id":"W2893066337","doi":"10.1007/s00500-018-3536-8","title":"Phasor particle swarm optimization: a simple and efficient variant of PSO","year":2018,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":262,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Particle swarm optimization; Phasor; Multi-swarm optimization; Benchmark (surveying); Mathematical optimization; Mathematics; Derivative-free optimization; Algorithm; Meta-optimization; Computer science; Electric power system; Power (physics)","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":[0.0007621873,0.0001048627,0.0001752926,0.00008429066,0.0002258991,0.0001404948,0.0004377567,0.00003480856,0.00005765384],"category_scores_gemma":[0.0004099382,0.00009953604,0.00002858715,0.0007004975,0.0001599997,0.0001029805,0.0005199716,0.00007920048,0.00001822207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001881543,"about_ca_system_score_gemma":0.00008206016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001493905,"about_ca_topic_score_gemma":5.205321e-7,"domain_scores_codex":[0.998509,0.0001130077,0.0003358804,0.0003614067,0.0003689574,0.0003117432],"domain_scores_gemma":[0.9986796,0.0002804296,0.0001324447,0.0003992266,0.0003772815,0.0001309727],"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":[0.00001096624,0.0001803549,0.0005413805,0.00004154054,0.00003179891,0.00001599815,0.002063356,0.9280584,0.0002591631,0.01575536,0.0002298913,0.05281173],"study_design_scores_gemma":[0.000426218,0.0001015189,0.0002602759,0.00001539346,0.000004496422,0.00002243134,0.00002573332,0.9965436,0.002143117,0.0001846466,0.0001688199,0.0001037226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02772792,0.00007038524,0.9710366,0.0002564326,0.0001744876,0.0001573186,0.000001479459,0.0001067886,0.0004685814],"genre_scores_gemma":[0.617903,0.000002223623,0.3819096,0.00006703238,0.0000796675,0.000001317649,9.778398e-7,0.000006805094,0.00002933287],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5901751,"threshold_uncertainty_score":0.4058962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02121247710130197,"score_gpt":0.2948523991375553,"score_spread":0.2736399220362534,"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."}}