{"id":"W2968765286","doi":"10.3390/en12163052","title":"An Improved Hybrid Particle Swarm Optimization and Tabu Search Algorithm for Expansion Planning of Large Dimension Electric Distribution Network","year":2019,"lang":"en","type":"article","venue":"Energies","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Tabu search; Particle swarm optimization; Mathematical optimization; Dimension (graph theory); Multi-swarm optimization; Algorithm; Computer science; Metaheuristic; Hybrid algorithm (constraint satisfaction); Mathematics","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":[0.0002336799,0.0001270056,0.0001660638,0.00003078425,0.00007855446,0.00003177287,0.00005976703,0.00006167084,0.000006850966],"category_scores_gemma":[0.00001251048,0.0001317295,0.00003192978,0.0001860308,0.0000127217,0.0003053197,0.00002641126,0.00007622917,0.000001635531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000578004,"about_ca_system_score_gemma":0.00001291506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008940085,"about_ca_topic_score_gemma":5.431951e-7,"domain_scores_codex":[0.9991274,0.00002644746,0.0001998554,0.0001885446,0.0001169835,0.0003407717],"domain_scores_gemma":[0.9996079,0.00005041882,0.00003717804,0.0001622085,0.00008422686,0.00005805015],"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":[0.00002703808,0.00002810766,0.0004799892,0.00003552483,0.00001282088,4.124493e-7,0.00005616174,0.9228223,0.07263578,0.0001273934,0.0001660218,0.003608453],"study_design_scores_gemma":[0.0004760951,0.0002277155,0.0009601301,0.00002460107,0.00001332868,0.00000158079,0.00006183522,0.8024329,0.1955757,0.0000175764,0.00008924522,0.0001192559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6173167,0.0004067,0.3817694,0.000004608998,0.0001348677,0.0001765671,0.00007707482,0.0001090726,0.000004958158],"genre_scores_gemma":[0.9895542,0.00006235275,0.009233896,0.000005230825,0.00007747218,0.00002146507,0.001010464,0.00002584045,0.000009042215],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3725355,"threshold_uncertainty_score":0.5371774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005108061115477131,"score_gpt":0.2295462160695984,"score_spread":0.2244381549541212,"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."}}