{"id":"W2606383594","doi":"10.1109/tii.2017.2695122","title":"Improved Random Drift Particle Swarm Optimization With Self-Adaptive Mechanism for Solving the Power Economic Dispatch Problem","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Electric Power System Optimization","field":"Engineering","cited_by":119,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Particle swarm optimization; Mathematical optimization; Economic dispatch; Crossover; Benchmark (surveying); Multi-swarm optimization; Position (finance); Computer science; Metaheuristic; Imperialist competitive algorithm; Meta-optimization; Power (physics); Electric power system; Algorithm; Mathematics; Artificial intelligence","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":[0.0003475809,0.0002450413,0.0002591867,0.0000743791,0.0007657853,0.0003857545,0.0003118854,0.0002141659,0.00001487924],"category_scores_gemma":[0.0000134254,0.0001873353,0.00008901924,0.00008978237,0.0000355642,0.0009758156,0.000002219694,0.0003228105,0.00001326123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002627173,"about_ca_system_score_gemma":0.0001135586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003689869,"about_ca_topic_score_gemma":0.00005979184,"domain_scores_codex":[0.9987834,0.00002148718,0.0005763561,0.0001170743,0.0001486615,0.0003530347],"domain_scores_gemma":[0.9988703,0.0001585332,0.0002892595,0.0004876732,0.0001093484,0.00008486622],"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":[0.0001792543,0.00002740051,0.000002221867,0.00002001455,0.0001821241,1.522783e-7,0.001266996,0.9967589,0.00003439313,0.0004631642,0.0001641483,0.0009011989],"study_design_scores_gemma":[0.004216126,0.0002967422,6.187498e-7,0.00005479039,0.0001254396,0.000005655205,0.0003288402,0.9707751,0.02385536,0.00003645219,0.00006107245,0.0002438151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008122041,0.00000290239,0.9876363,0.0001247734,0.00112249,0.001902272,0.00004847272,0.0003060682,0.0007346651],"genre_scores_gemma":[0.9751917,0.00002054183,0.02408331,0.0000296963,0.00008822508,0.0004538737,0.000004949924,0.00005393698,0.0000738078],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9670696,"threshold_uncertainty_score":0.7639312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01552880190210809,"score_gpt":0.2090435726441396,"score_spread":0.1935147707420315,"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."}}