{"id":"W4319299347","doi":"10.1002/ett.4739","title":"A levy flight based strategy to improve the exploitation capability of arithmetic optimization algorithm for engineering global optimization problems","year":2023,"lang":"en","type":"article","venue":"Transactions on Emerging Telecommunications Technologies","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Lévy flight; Algorithm; Meta-optimization; Firefly algorithm; Metaheuristic; Mathematical optimization; Derivative-free optimization; Engineering optimization; Population-based incremental learning; Multi-swarm optimization; Optimization problem; Imperialist competitive algorithm; Maxima and minima; Continuous optimization; Global optimization; Computer science; Particle swarm optimization; Cuckoo search; Mathematics; Genetic 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":[0.0009468286,0.0002416773,0.0002610112,0.0007899515,0.0005867293,0.0001646996,0.002040124,0.0001560809,0.00001681421],"category_scores_gemma":[0.0005992315,0.0002182145,0.0001273568,0.005119004,0.0001350041,0.0004055368,0.00008274504,0.000292536,0.000009913996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000213491,"about_ca_system_score_gemma":0.0001722348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003898846,"about_ca_topic_score_gemma":0.00001191671,"domain_scores_codex":[0.9978793,0.0001407934,0.0006411521,0.000500564,0.000423751,0.0004144183],"domain_scores_gemma":[0.9962864,0.0007477182,0.0001968939,0.002031607,0.0006788195,0.00005854803],"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":[0.000003357524,0.0001109105,0.000001592174,0.00002832888,0.00002765373,1.053075e-7,0.00009922471,0.8062872,0.00007461792,0.001294898,0.00003917995,0.1920329],"study_design_scores_gemma":[0.0003469979,0.0002400853,0.00002347692,0.0000434682,0.00002383105,0.000001211557,0.0004057686,0.9948747,0.00302122,0.0006163954,0.000197453,0.000205444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00006743185,0.00006476798,0.9882407,0.007551408,0.0001496024,0.001753914,0.00009183433,0.002028145,0.00005215614],"genre_scores_gemma":[0.07059237,0.0002154973,0.9270326,0.0000257244,0.000006168774,0.001998452,0.00005401764,0.00002666504,0.00004844551],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1918275,"threshold_uncertainty_score":0.8898529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03127238380005774,"score_gpt":0.2990999081987701,"score_spread":0.2678275243987124,"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."}}