{"id":"W2553081833","doi":"10.1007/s10458-016-9350-8","title":"A novel abstraction for swarm intelligence: particle field optimization","year":2016,"lang":"en","type":"article","venue":"Autonomous Agents and Multi-Agent Systems","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Particle swarm optimization; Multi-swarm optimization; Abstraction; Swarm intelligence; Swarm behaviour; Computer science; Perspective (graphical); Metaheuristic; Field (mathematics); Set (abstract data type); Heuristic; Mathematical optimization; Artificial intelligence; 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":[0.001002981,0.001220747,0.001506794,0.001198754,0.0006383315,0.001827057,0.001416426,0.001143722,0.002179078],"category_scores_gemma":[0.002505212,0.000397494,0.001159806,0.002089774,0.001208431,0.00211455,0.002067982,0.002737986,0.0007561853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006092945,"about_ca_system_score_gemma":0.0007401271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001541482,"about_ca_topic_score_gemma":0.001438332,"domain_scores_codex":[0.9994276,0.0002574927,0.00003139054,0.00006307453,0.0001882207,0.00003229132],"domain_scores_gemma":[0.9995711,0.0001785798,0.00003830324,0.0001050393,0.00007226529,0.00003479978],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000457835,0.00006864801,0.000540472,0.0002402427,0.000109157,0.00008021931,0.0001492544,0.1949762,0.002193011,0.6763718,0.006633976,0.1185912],"study_design_scores_gemma":[0.00002058736,0.00003493035,0.0001459298,0.00002973893,0.00002973643,0.00005508133,0.00002281631,0.7866739,0.0005379693,0.1979006,0.01453205,0.00001655749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001359629,0.0006990258,0.9935355,0.0002085223,0.0001479604,0.00002125543,0.00003048063,0.0001116569,0.003886007],"genre_scores_gemma":[0.1605591,0.003629663,0.8268811,0.0003630213,0.000708858,0.0003048089,0.0002334796,0.000292488,0.007027423],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002179078,"threshold_uncertainty_score":0.007289767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07563508047408636,"score_gpt":0.3298468779985365,"score_spread":0.2542117975244502,"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."}}