{"id":"W1988969082","doi":"10.1016/j.adhoc.2008.06.001","title":"HOPNET: A hybrid ant colony optimization routing algorithm for mobile ad hoc network","year":2008,"lang":"en","type":"article","venue":"Ad Hoc Networks","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":234,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Destination-Sequenced Distance Vector routing; Computer science; Ant colony optimization algorithms; Link-state routing protocol; Distance-vector routing protocol; Hybrid routing; Distributed computing; Computer network; Wireless Routing Protocol; Dynamic Source Routing; Optimized Link State Routing Protocol; Mobile ad hoc network; Ad hoc On-Demand Distance Vector Routing; Routing protocol; Static routing; Swarm intelligence; Algorithm; Network packet; Particle swarm optimization","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":[0.0009280206,0.0006407422,0.0007647125,0.0001148988,0.001115738,0.0002980182,0.00168842,0.0003466183,0.00005233729],"category_scores_gemma":[0.00004337189,0.0006761214,0.0003485202,0.001232147,0.0001896935,0.0008784137,0.0006662132,0.0006409998,0.00003809843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002411867,"about_ca_system_score_gemma":0.0002140061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005515374,"about_ca_topic_score_gemma":0.00002053847,"domain_scores_codex":[0.9949992,0.0002293885,0.0009429745,0.001419582,0.0005679792,0.001840852],"domain_scores_gemma":[0.9964656,0.0007189176,0.0005665388,0.001489598,0.0003415469,0.000417796],"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":[0.00002705147,0.00007256824,0.00005109591,0.000005627157,0.00005029779,0.0000726151,0.0001637029,0.6389969,0.000001197027,0.0002233963,0.02751503,0.3328205],"study_design_scores_gemma":[0.001204503,0.0006208036,0.0001020729,0.00009870759,0.00003665891,0.0002188142,0.00001628594,0.9096516,0.00001435958,0.0002718896,0.08705397,0.0007103726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001133415,0.02718832,0.9657019,0.000141229,0.002543956,0.002096976,0.00001454719,0.0007887708,0.0003908674],"genre_scores_gemma":[0.0819372,0.01663848,0.8915144,0.00206985,0.003808107,0.00190591,0.0002706194,0.000225259,0.001630126],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3321101,"threshold_uncertainty_score":0.999569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0117247028281354,"score_gpt":0.2278753296525496,"score_spread":0.2161506268244142,"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."}}