{"id":"W2060611557","doi":"10.1155/2013/749256","title":"Predatory Search Strategy Based on Swarm Intelligence for Continuous Optimization Problems","year":2013,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Fundamental Research Funds for the Central Universities; Hong Kong Polytechnic University; East China University of Science and Technology; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Swarm intelligence; Particle swarm optimization; Mathematical optimization; Computer science; Multi-swarm optimization; Variable (mathematics); Swarm behaviour; Metaheuristic; Variable neighborhood search; Artificial intelligence; 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":[],"consensus_categories":[],"category_scores_codex":[0.001124563,0.0002288922,0.0003065682,0.0003772537,0.00005463218,0.0003672432,0.0008115699,0.0001177566,0.0001501982],"category_scores_gemma":[0.0006901482,0.0002099792,0.00005877731,0.0006382786,0.00004057806,0.0004171594,0.0001339509,0.0003187744,0.00007519022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001124687,"about_ca_system_score_gemma":0.00007963784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006174198,"about_ca_topic_score_gemma":2.92334e-7,"domain_scores_codex":[0.997706,0.00006531544,0.0006145473,0.0004840844,0.0005509803,0.000579052],"domain_scores_gemma":[0.9981886,0.0007535053,0.000061528,0.000562984,0.0002544044,0.0001789979],"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":[0.000001179353,0.0001593105,0.000004781769,0.000650532,0.000006263726,0.000001313013,0.0001648575,0.9817041,0.00007099556,0.01413848,0.00004210261,0.003056062],"study_design_scores_gemma":[0.0002666077,0.0001629927,0.000005890815,0.0002332268,0.000001924101,0.000002625269,0.00001524524,0.9938707,0.0005904148,0.004588824,0.00004145277,0.0002200351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00009294027,0.00002735804,0.9961023,0.000231234,0.00007936752,0.002097602,0.000002940117,0.0002291311,0.001137155],"genre_scores_gemma":[0.2875845,0.000007202972,0.7110317,0.00004096274,0.00003024375,0.001018889,0.000007960646,0.00004438016,0.0002341339],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2874916,"threshold_uncertainty_score":0.8562703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03208382069360572,"score_gpt":0.2664878477875366,"score_spread":0.2344040270939309,"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."}}