{"id":"W2548265241","doi":"10.1007/s11721-016-0128-z","title":"Inertia weight control strategies for particle swarm optimization","year":2016,"lang":"en","type":"article","venue":"Swarm Intelligence","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":103,"is_retracted":false,"has_abstract":false,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inertia; Benchmark (surveying); Particle swarm optimization; Computer science; Control (management); Mathematical optimization; Selection (genetic algorithm); Convergence (economics); Population; Control theory (sociology); Mathematics; Artificial intelligence; Economics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005557633,0.000888413,0.000583626,0.0006794948,0.0004164301,0.0009745818,0.0009978276,0.0009098042,0.002033972],"category_scores_gemma":[0.002009542,0.0003146821,0.0003336275,0.0007113215,0.0005717474,0.0009407623,0.0007296195,0.0008656163,0.0004488809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003609911,"about_ca_system_score_gemma":0.000442005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003388107,"about_ca_topic_score_gemma":0.002415521,"domain_scores_codex":[0.9998304,0.00004505238,0.00001125748,0.0000151429,0.00008206468,0.00001614],"domain_scores_gemma":[0.9997359,0.00009638416,0.00003589413,0.0000235738,0.00009353918,0.0000146654],"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":[0.00009177937,0.0001131481,0.0003402437,0.0001643653,0.00006250702,0.00008182457,0.0001203518,0.7728268,0.004630705,0.05550225,0.003452151,0.1626139],"study_design_scores_gemma":[0.00001668522,0.00003555603,0.00007182405,0.00001055357,0.000009218008,0.000009275645,0.000008289789,0.9931525,0.0003969869,0.005084356,0.001199805,0.000004902594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01374502,0.001414104,0.9725405,0.0002493576,0.0002987066,0.00007116289,0.00001629858,0.0001436099,0.01152118],"genre_scores_gemma":[0.7553723,0.001878168,0.2253932,0.0002099857,0.0003102885,0.0003807478,0.00008953259,0.0001685496,0.01619718],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003388107,"threshold_uncertainty_score":0.006804347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02980599871837959,"score_gpt":0.2991908573740253,"score_spread":0.2693848586556458,"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."}}