{"id":"W4283692374","doi":"10.1016/j.cma.2022.115223","title":"An effective multi-objective artificial hummingbird algorithm with dynamic elimination-based crowding distance for solving engineering design problems","year":2022,"lang":"en","type":"article","venue":"Computer Methods in Applied Mechanics and Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":149,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Mathematical optimization; Sorting; Benchmark (surveying); Computer science; Convergence (economics); Multi-objective optimization; Pareto principle; Algorithm; Hummingbird; Search-based software engineering; Metaheuristic; Population; Mathematics; Software; Software design; Software development","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00157351,0.0004024009,0.0004219168,0.0005098931,0.0003511128,0.0001861664,0.0005055654,0.0000789508,9.322815e-7],"category_scores_gemma":[0.00005555437,0.0004564394,0.00005262387,0.0008843871,0.00001254432,0.0003069586,0.0002420881,0.000435267,1.571313e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000498452,"about_ca_system_score_gemma":0.00005328309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002855654,"about_ca_topic_score_gemma":0.000001776162,"domain_scores_codex":[0.9977652,0.0001096817,0.0003840419,0.0009482748,0.0002485474,0.0005442576],"domain_scores_gemma":[0.9982787,0.0009380889,0.0001639308,0.0003821974,0.0001098308,0.0001272932],"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":[0.0000162876,0.00007409776,4.784784e-7,0.00004988618,0.00001992893,0.000005144315,0.000715026,0.7486404,0.01111385,0.01354151,1.244777e-7,0.2258232],"study_design_scores_gemma":[0.001119588,0.0003253479,0.00003438636,0.00005563695,0.000013269,0.00001135188,0.0000837896,0.9905066,0.005973464,0.00126902,0.00004284007,0.0005646448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00004457434,0.00009969966,0.9971126,0.00001409973,0.000513548,0.001804438,0.0000100087,0.000399674,0.000001345944],"genre_scores_gemma":[0.04712197,0.000003817773,0.9508275,0.00003416296,0.00004929352,0.001861386,0.00001207873,0.0000884703,0.000001272352],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2418662,"threshold_uncertainty_score":0.9997888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01611159263480726,"score_gpt":0.2848786785389575,"score_spread":0.2687670859041503,"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."}}