{"id":"W4313644256","doi":"10.1371/journal.pone.0279572","title":"A novel hybrid PSO based on levy flight and wavelet mutation for global optimization","year":2023,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Optical Wireless Communication Technologies","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Polit National Laboratory for Marine Science and Technology; National Natural Science Foundation of China","keywords":"Lévy flight; Mutation; Wavelet; Computer science; Particle swarm optimization; Computational biology; Biology; Genetics; Artificial intelligence; Mathematics; Algorithm; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004598987,0.0006726922,0.0009277416,0.0007181492,0.0003470033,0.0006868771,0.001196817,0.0008715325,0.001194489],"category_scores_gemma":[0.0008948385,0.0002880627,0.0008496625,0.000760201,0.0003639835,0.0008727987,0.0007338288,0.0006381022,0.0002968003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003376142,"about_ca_system_score_gemma":0.0006910246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002163474,"about_ca_topic_score_gemma":0.001773128,"domain_scores_codex":[0.9996855,0.00005290012,0.00001853784,0.00005376588,0.0001558134,0.0000334263],"domain_scores_gemma":[0.9997891,0.00007400763,0.00002566333,0.00001958088,0.00007213499,0.00001958867],"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":[0.0001694726,0.0001878639,0.002741512,0.0003236738,0.0002774182,0.0003450398,0.0001548882,0.5680656,0.02872839,0.02646594,0.004675027,0.3678651],"study_design_scores_gemma":[0.00002863766,0.0000605957,0.0002584833,0.000005995554,0.00001566577,0.0000883375,0.00000770909,0.9955354,0.001101764,0.001205993,0.001680199,0.0000111963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01460712,0.0004817726,0.9811264,0.0001003421,0.0001135811,0.00005386191,0.00002094426,0.0003238987,0.003172142],"genre_scores_gemma":[0.526589,0.000921457,0.4639608,0.0002364236,0.0001269704,0.0003845315,0.0001654756,0.0001339636,0.007481354],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002163474,"threshold_uncertainty_score":0.004301727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04174763809861262,"score_gpt":0.2302234605513735,"score_spread":0.1884758224527609,"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."}}