{"id":"W2026243033","doi":"10.1260/1748-3018.9.2.143","title":"Quantum-Behaved Particle Swarm Optimization with Novel Adaptive Strategies","year":2015,"lang":"en","type":"article","venue":"Journal of Algorithms & Computational Technology","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Particle swarm optimization; Benchmark (surveying); Multi-swarm optimization; Position (finance); Attractor; Metaheuristic; Mathematical optimization; Swarm behaviour; Computer science; Swarm intelligence; Global optimization; Mathematics; Algorithm","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":[0.0008444218,0.0002202612,0.0003970179,0.0007444915,0.000122083,0.000253355,0.001081596,0.0001462763,0.0000153391],"category_scores_gemma":[0.000283307,0.000180427,0.00006716231,0.001848672,0.0002688549,0.001239843,0.0002184668,0.0004610626,0.00001404113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001782609,"about_ca_system_score_gemma":0.001396675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000749431,"about_ca_topic_score_gemma":0.000001156145,"domain_scores_codex":[0.9972178,0.000104794,0.0007410463,0.0003363698,0.00122715,0.0003728438],"domain_scores_gemma":[0.995145,0.0002118378,0.0006621588,0.0003184076,0.003397944,0.0002646072],"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":[0.00005421677,0.0002463129,0.0000655823,0.000003259273,0.0000787774,0.00009260469,0.0002042691,0.9055199,0.00003002322,0.08835609,0.0001493387,0.00519965],"study_design_scores_gemma":[0.00204981,0.00133086,0.0001475275,0.00002762256,0.00002006891,0.001012526,0.0007399473,0.9727198,0.0003668714,0.02120323,0.0001814444,0.0002002439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003873432,0.0001838461,0.9920036,0.003172073,0.0002831526,0.000189058,0.000006071109,0.0001490232,0.0001398046],"genre_scores_gemma":[0.2181863,0.00001229982,0.7815942,0.00006240642,0.00007795383,0.000008403319,0.000003955461,0.00001768343,0.00003683462],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2143128,"threshold_uncertainty_score":0.73576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05256136336976503,"score_gpt":0.3081965978010415,"score_spread":0.2556352344312765,"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."}}