{"id":"W2605533099","doi":"10.1016/j.swevo.2017.04.005","title":"Micro-time variant multi-objective particle swarm optimization (micro-TVMOPSO) of a solar thermal combisystem","year":2017,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Economic Affairs; Concordia University","keywords":"Benchmark (surveying); Computer science; Multi-objective optimization; Mathematical optimization; Pareto principle; Particle swarm optimization; Evolutionary algorithm; Multi-swarm optimization; Metaheuristic; Optimization problem; Population; Engineering optimization; Algorithm; Artificial intelligence; Machine learning; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003372707,0.0002645726,0.00033568,0.0001405826,0.0009232129,0.0002011822,0.0005193622,0.0001217185,0.000008979341],"category_scores_gemma":[0.0001308267,0.0002745086,0.00008660372,0.0002352931,0.0002440148,0.001671044,0.0003738118,0.0001470586,0.0000284727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001485756,"about_ca_system_score_gemma":0.0001403811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007418539,"about_ca_topic_score_gemma":0.000002395998,"domain_scores_codex":[0.9980431,0.0001993122,0.0004919197,0.0006186903,0.0003092398,0.0003377994],"domain_scores_gemma":[0.9979518,0.0001334119,0.0006270033,0.0005298989,0.0006246894,0.000133165],"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":[0.00006554544,0.0003799052,0.001794095,0.00004574425,0.00008014115,0.00001924131,0.001676968,0.9822163,0.006739768,0.0009952205,0.00005764438,0.005929443],"study_design_scores_gemma":[0.001937678,0.000134363,0.03138254,0.00005677876,0.00002279818,0.00006720157,0.00008889949,0.9594929,0.005824422,0.000681961,0.0000158294,0.0002945947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05645408,0.0002402878,0.9416961,0.0002931227,0.0003817366,0.0005311916,0.0000257632,0.0001660996,0.0002116361],"genre_scores_gemma":[0.5594384,0.00002305181,0.4402634,0.00004076598,0.00004686926,0.00002062759,0.00002866416,0.00001847536,0.0001197115],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5029843,"threshold_uncertainty_score":0.9999707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01362871856067218,"score_gpt":0.2526617303293274,"score_spread":0.2390330117686552,"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."}}