{"id":"W2890194468","doi":"10.20944/preprints201809.0329.v1","title":"A Simple Ant Colony Optimization Algorithm to Select Cluster Heads in Ad Hoc Networks","year":2018,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Cluster analysis; Ant colony optimization algorithms; Scalability; Wireless ad hoc network; Cluster (spacecraft); Algorithm; Simple (philosophy); Selection (genetic algorithm); k-medoids; Stability (learning theory); Correlation clustering; CURE data clustering algorithm; Artificial intelligence; Computer network; Machine learning","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":[0.0006246738,0.0008071316,0.001087081,0.000586124,0.000591632,0.0006616949,0.001497871,0.001059621,0.0008076452],"category_scores_gemma":[0.001854205,0.0003490943,0.000374185,0.00124824,0.0006140182,0.0008143682,0.0006692156,0.0007141703,0.0003202265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003916365,"about_ca_system_score_gemma":0.0008599009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002455163,"about_ca_topic_score_gemma":0.002973373,"domain_scores_codex":[0.999383,0.000186123,0.0000300277,0.000103785,0.0002677052,0.00002934677],"domain_scores_gemma":[0.9994929,0.0002142763,0.00006097616,0.00005161623,0.0001527159,0.00002741427],"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":[0.0000622683,0.0000780257,0.0006262774,0.0001466564,0.00006823994,0.00007557997,0.00008916919,0.8127595,0.007885065,0.01170288,0.002603115,0.1639034],"study_design_scores_gemma":[0.00002159752,0.00004985908,0.000131555,0.000004395328,0.000009742434,0.00003237827,0.00000807342,0.9943751,0.00117596,0.002613547,0.001569546,0.000008201481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01033254,0.0005387129,0.9864432,0.0001028878,0.00008404855,0.0001372862,0.00002412498,0.0003705566,0.001966691],"genre_scores_gemma":[0.3181824,0.0007132625,0.6754961,0.0001383459,0.0001031012,0.0003723368,0.0001441815,0.0001056001,0.004744577],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002455163,"threshold_uncertainty_score":0.00488174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04594270196105719,"score_gpt":0.3168136811283892,"score_spread":0.270870979167332,"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."}}