{"id":"W2084165293","doi":"10.1162/evco.2010.18.1.18105","title":"Strength Pareto Particle Swarm Optimization and Hybrid EA-PSO for Multi-Objective Optimization","year":2010,"lang":"en","type":"article","venue":"Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":127,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Particle swarm optimization; Evolutionary algorithm; Mathematical optimization; Pareto principle; Multi-swarm optimization; Convergence (economics); Imperialist competitive algorithm; Multi-objective optimization; Computer science; Metaheuristic; Mathematics; Algorithm","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":[0.0002668636,0.0002872378,0.0002324822,0.000204797,0.0005726088,0.0001774531,0.0002887402,0.0001104889,0.00001423683],"category_scores_gemma":[0.0003514225,0.0003226809,0.00007174881,0.0005497517,0.0001337101,0.001877922,0.0001597409,0.0002205408,0.00000953096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001499491,"about_ca_system_score_gemma":0.0001470898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001504783,"about_ca_topic_score_gemma":0.00001225267,"domain_scores_codex":[0.9979458,0.0001120448,0.0004447569,0.0007999396,0.0003186546,0.0003787593],"domain_scores_gemma":[0.9981132,0.0002891363,0.0002979272,0.0003171945,0.0008023201,0.000180189],"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":[0.00002386605,0.0001920075,0.0003072751,0.00001169378,0.0000215363,0.000002152741,0.000308235,0.9873229,0.0002141294,0.004065083,0.0001156514,0.007415522],"study_design_scores_gemma":[0.001820532,0.0001559819,0.002771425,0.00001228228,0.00001738613,0.00004308869,0.00006685068,0.9925848,0.0008925755,0.001172678,0.00009326395,0.000369104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006141757,0.00009438154,0.9909244,0.0003655588,0.0008696435,0.001029777,0.00004218265,0.0004693581,0.00006291154],"genre_scores_gemma":[0.2819073,0.00002170283,0.7174283,0.00009642571,0.0001145513,0.000131976,0.0002036962,0.00002922336,0.00006687657],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2757655,"threshold_uncertainty_score":0.9999225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01579169677716093,"score_gpt":0.2768926140270181,"score_spread":0.2611009172498572,"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."}}