{"id":"W4413776701","doi":"10.2139/ssrn.5407188","title":"An Adaptive Balance Search  Based Complementary Heterogeneous Particle Swarm Optimization Architecture","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Particle swarm optimization; Architecture; Balance (ability); Computer science; Mathematical optimization; Multi-swarm optimization; Swarm behaviour; Particle (ecology); Artificial intelligence; Mathematics; Geography; Biology; Ecology","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003180944,0.0004431278,0.0004804803,0.0004608578,0.0004757503,0.0006796971,0.003216649,0.0002105215,0.0001119407],"category_scores_gemma":[0.00008596621,0.0004348901,0.0002132799,0.0006892335,0.00009865776,0.0002832748,0.0009770719,0.005049659,0.00001373248],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001646653,"about_ca_system_score_gemma":0.009389198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000129975,"about_ca_topic_score_gemma":0.0001449706,"domain_scores_codex":[0.9927471,0.001303288,0.0006836813,0.0009961659,0.001287125,0.002982694],"domain_scores_gemma":[0.9972289,0.0001890098,0.0003051381,0.00126093,0.000668705,0.0003472735],"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":[0.00007748554,0.0001928655,0.00008733017,0.00002737579,0.0001858635,0.00002326851,0.0001467308,0.966839,0.00001308779,0.01088769,0.00003288429,0.02148646],"study_design_scores_gemma":[0.0008072023,0.0005606464,0.00001502961,0.00006644293,0.00003248433,0.0001136214,0.00008164132,0.9746693,0.0005369255,0.02266618,0.00008181344,0.000368757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001065088,0.001073699,0.9942792,0.002110658,0.0004403523,0.0006528577,0.00005779496,0.0001655791,0.0001547399],"genre_scores_gemma":[0.3647905,0.001729619,0.631278,0.0007137168,0.0004721613,0.00008522529,0.0002001173,0.00006326908,0.0006674538],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3637254,"threshold_uncertainty_score":0.9998103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02224870955929856,"score_gpt":0.3040933373732065,"score_spread":0.2818446278139079,"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."}}