{"id":"W2359483975","doi":"","title":"The Performance Analysis of Particle Swarm Optimization for Solving Continuous Optimization Problem","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multi-swarm optimization; Particle swarm optimization; Benchmark (surveying); Mathematical optimization; Computer science; Metaheuristic; Meta-optimization; Derivative-free optimization; Imperialist competitive algorithm; Swarm behaviour; Optimization problem; Population; Function (biology); Algorithm; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002210238,0.0009316942,0.0008572842,0.0007399857,0.0004345174,0.001150806,0.000696671,0.001168229,0.001740019],"category_scores_gemma":[0.006077185,0.0002387337,0.0006706173,0.001261447,0.0006687766,0.001247456,0.0005402314,0.001189349,0.0005431449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000735827,"about_ca_system_score_gemma":0.00099401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003438766,"about_ca_topic_score_gemma":0.0008260179,"domain_scores_codex":[0.9986157,0.0003936331,0.00006351408,0.0001217668,0.0007418027,0.000063558],"domain_scores_gemma":[0.9984119,0.001027019,0.00009300435,0.00009049106,0.000351387,0.00002628166],"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":[0.00008928754,0.00004829507,0.001284189,0.0004249288,0.0001202082,0.0001228063,0.00008055479,0.795597,0.003553478,0.05894667,0.003667755,0.1360649],"study_design_scores_gemma":[0.000006641648,0.00004877484,0.0004871388,0.00002350137,0.00001553867,0.00005007433,0.000008882813,0.9880813,0.001161838,0.006702204,0.003402785,0.00001123135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008584578,0.00536431,0.9717459,0.0004897483,0.0002216327,0.00004630034,0.00004996945,0.0002992585,0.01319835],"genre_scores_gemma":[0.7285467,0.01049839,0.2511692,0.0002705885,0.0007275345,0.0002873667,0.0005087712,0.0003106428,0.007680912],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003438766,"threshold_uncertainty_score":0.01168901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01764427329822725,"score_gpt":0.2615115871013189,"score_spread":0.2438673138030916,"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."}}