{"id":"W2779495064","doi":"10.5539/mas.v12n1p148","title":"Movement Particle Swarm Optimization Algorithm","year":2017,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Particle swarm optimization; Multi-swarm optimization; Metaheuristic; Maxima and minima; Benchmark (surveying); Mathematical optimization; Derivative-free optimization; Algorithm; Trigonometric functions; Computer science; Firefly algorithm; Heuristic; Sine; Optimization problem; Meta-optimization; Meta heuristic; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001546774,0.0001593851,0.0001622547,0.0001372317,0.001930785,0.002262403,0.004304301,0.00004108718,0.0000515847],"category_scores_gemma":[0.0002120984,0.0001497082,0.00003275298,0.0004920791,0.0006511115,0.001322956,0.001484704,0.0001408805,0.0001468375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001220039,"about_ca_system_score_gemma":0.0002952116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002121199,"about_ca_topic_score_gemma":0.000001233447,"domain_scores_codex":[0.9967433,0.00002436437,0.0002726939,0.000851723,0.001457003,0.0006509366],"domain_scores_gemma":[0.9970042,0.00003659454,0.0001958618,0.002152928,0.0002855129,0.0003248934],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005856324,0.0002108027,0.0000953309,0.000008745912,0.00001107677,0.00001574366,0.0008060129,0.3021311,0.02279633,0.08995192,0.0001672746,0.5837998],"study_design_scores_gemma":[0.0003379118,0.00001922861,0.0004662432,0.000003323881,0.000002059953,0.000001735328,0.000009638679,0.9699562,0.02246949,0.006453147,0.0001018272,0.0001792521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002828527,0.00001516959,0.9842221,0.000913163,0.0002817095,0.0003307015,0.000002196378,0.0001595544,0.01379254],"genre_scores_gemma":[0.3236622,0.00001382826,0.6753241,0.0002986836,0.00004518773,0.00005631353,9.984774e-7,0.00001008801,0.0005886548],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.667825,"threshold_uncertainty_score":0.9993685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02916682746625949,"score_gpt":0.297662998130203,"score_spread":0.2684961706639435,"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."}}