{"id":"W4205737100","doi":"10.1109/smc52423.2021.9659146","title":"Reference Point-Based Particle Sub-Swarm Optimization","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Particle swarm optimization; Multi-swarm optimization; Mathematical optimization; Equidistant; Maxima and minima; Position (finance); Multi-objective optimization; Metaheuristic; Computation; Computer science; Swarm behaviour; Pareto principle; Point (geometry); Optimization problem; Algorithm; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009580202,0.0008918585,0.00123151,0.0008355074,0.0004764091,0.001043058,0.001501809,0.0009330853,0.002622003],"category_scores_gemma":[0.001474234,0.0003680942,0.0007169454,0.0009873012,0.000525776,0.0009041004,0.0008803168,0.0008233096,0.0009638398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005746826,"about_ca_system_score_gemma":0.0008956254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002872562,"about_ca_topic_score_gemma":0.002040331,"domain_scores_codex":[0.9993723,0.0001653677,0.00002594507,0.00009261775,0.0003060383,0.00003770312],"domain_scores_gemma":[0.9995895,0.0001390779,0.00004827037,0.00004730763,0.0001529235,0.00002302582],"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":[0.00008289119,0.00005563449,0.0005018776,0.0001480324,0.00007214209,0.00007078709,0.00007541976,0.8190206,0.004088666,0.02231535,0.002657122,0.1509115],"study_design_scores_gemma":[0.000008753493,0.00003673242,0.00007785654,0.00000609779,0.00000700979,0.00001588985,0.000005310822,0.9949008,0.000662494,0.001761987,0.002511665,0.000005411727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003426732,0.0003002578,0.9926915,0.00005060297,0.00005386925,0.00003887999,0.00001851208,0.0002073652,0.003212369],"genre_scores_gemma":[0.3408929,0.00107176,0.645807,0.0001560354,0.0001044257,0.0004653971,0.0003676834,0.0002095468,0.01092518],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002872562,"threshold_uncertainty_score":0.008771479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04976894851196202,"score_gpt":0.2923497905512877,"score_spread":0.2425808420393257,"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."}}