{"id":"W1523796837","doi":"10.1007/978-3-642-11842-5_73","title":"A Hybrid Particle Swarm Optimization Algorithm Based on Space Transformation Search and a Modified Velocity Model","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Particle swarm optimization; Benchmark (surveying); Multi-swarm optimization; Computer science; Mathematical optimization; Transformation (genetics); Local optimum; Metaheuristic; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001726753,0.0004510254,0.0004290158,0.0008424156,0.0004105236,0.0008671596,0.001636355,0.0002700068,0.00002047496],"category_scores_gemma":[0.0001611141,0.0004282315,0.00007817803,0.0006031311,0.0006223087,0.0007483672,0.0004481825,0.001198123,0.00001437272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002278318,"about_ca_system_score_gemma":0.0008676388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000206622,"about_ca_topic_score_gemma":0.000007521341,"domain_scores_codex":[0.9955887,0.00008554126,0.000502316,0.001341769,0.001801574,0.0006801161],"domain_scores_gemma":[0.9974582,0.0004208338,0.0001378473,0.001117019,0.0005302606,0.0003358994],"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":[0.000007502899,0.00003262043,5.296522e-7,0.00002078082,0.000002902402,0.00001292634,0.0003100329,0.7570735,0.00002274996,0.003844704,0.000001070163,0.2386707],"study_design_scores_gemma":[0.0005533355,0.0001492963,0.00000460977,0.00008850603,0.000005649344,0.00001935808,1.059123e-7,0.982623,0.004477621,0.01163653,0.00001744179,0.0004245773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00005966831,0.00003309332,0.9960283,0.001710115,0.0003335767,0.0007443681,0.00001740123,0.0001518111,0.0009216717],"genre_scores_gemma":[0.04253599,0.00003972268,0.9565268,0.0005994684,0.00009489256,0.00001963546,0.00001595122,0.00003368241,0.0001338475],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2382461,"threshold_uncertainty_score":0.999817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02633687777795681,"score_gpt":0.2683005472629652,"score_spread":0.2419636694850084,"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."}}