{"id":"W1835894572","doi":"10.1109/ccece.2002.1013043","title":"Enhancing the particle swarm optimizer via proper parameters selection","year":2003,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Particle swarm optimization; Range (aeronautics); Mathematical optimization; Selection (genetic algorithm); Swarm intelligence; Computer science; Swarm behaviour; Multi-swarm optimization; Process (computing); Metaheuristic; Algorithm; Mathematics; Artificial intelligence; Engineering","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":[0.001132203,0.0001068826,0.0001046137,0.00003672369,0.0002494474,0.0003067308,0.0004859494,0.00003533514,0.0003137958],"category_scores_gemma":[0.0004124177,0.00006488612,0.00004419752,0.0007961054,0.0000541674,0.0003655626,0.00008851515,0.0001558703,0.0003270822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004584807,"about_ca_system_score_gemma":0.00009994079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003249187,"about_ca_topic_score_gemma":0.000007918131,"domain_scores_codex":[0.9982819,0.0003499083,0.0002430198,0.0003290371,0.0004303117,0.0003658076],"domain_scores_gemma":[0.9990237,0.0001877296,0.00004716676,0.0004496547,0.0001808088,0.0001108785],"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":[0.00004222948,0.0008969784,0.00176762,0.00005701972,0.0002872022,0.00003632702,0.003891235,0.5299599,0.03405006,0.3187978,0.008673845,0.1015398],"study_design_scores_gemma":[0.0001953567,0.000043088,0.00005491573,0.000002211616,0.000003835894,0.0000242318,0.00002888471,0.8009429,0.1963174,0.0005436531,0.001734867,0.0001086652],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004337983,0.00002697906,0.9891264,0.0009854585,0.0001952598,0.0003011387,8.821186e-8,0.0001462385,0.004880389],"genre_scores_gemma":[0.3198309,0.00000940457,0.6744447,0.0005184526,0.00001714867,0.00005697033,2.651934e-7,0.00001073701,0.005111423],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3182542,"threshold_uncertainty_score":0.4204089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02582213623270858,"score_gpt":0.2681246103243953,"score_spread":0.2423024740916868,"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."}}