{"id":"W2358818510","doi":"","title":"Reviews of the Meta-heuristic Algorithms for TSP","year":2006,"lang":"en","type":"article","venue":"","topic":"Power Systems and Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"CAE (Canada)","funders":"","keywords":"Tabu search; Travelling salesman problem; Simulated annealing; Ant colony optimization algorithms; Metaheuristic; Mathematical optimization; Meta heuristic; Computer science; Heuristic; Particle swarm optimization; Parallel metaheuristic; Genetic algorithm; Extremal optimization; Artificial neural network; Algorithm; Meta-optimization; Artificial intelligence; 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":[0.0007740175,0.001291224,0.001141081,0.001778875,0.0005671502,0.00144563,0.001575784,0.001165985,0.009317079],"category_scores_gemma":[0.00239856,0.0005303189,0.001057772,0.00401678,0.0002947516,0.001516908,0.0005352326,0.001163862,0.005984422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008257665,"about_ca_system_score_gemma":0.001104218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00270153,"about_ca_topic_score_gemma":0.003618701,"domain_scores_codex":[0.9992868,0.0002053487,0.00009229894,0.00007674308,0.0002997178,0.00003901123],"domain_scores_gemma":[0.9993083,0.0003269398,0.00005517872,0.00005031944,0.0002381177,0.0000210588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008045399,0.00009939405,0.0003865956,0.004024054,0.0001733159,0.0002534816,0.0001129001,0.05697308,0.001774358,0.03631286,0.04738525,0.8524243],"study_design_scores_gemma":[0.00005079925,0.0001417203,0.0007885842,0.001716025,0.0001797859,0.000873051,0.0001217328,0.08205572,0.002004918,0.05064621,0.861355,0.00006646272],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.003673961,0.5947142,0.3200319,0.002502989,0.002122987,0.0002463215,0.0006365437,0.0008916616,0.0751795],"genre_scores_gemma":[0.04361179,0.5440383,0.3758728,0.001244731,0.002038965,0.0004758539,0.001616705,0.0003762867,0.03072456],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.009317079,"threshold_uncertainty_score":0.0311687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04700734658145899,"score_gpt":0.2397030181427817,"score_spread":0.1926956715613227,"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."}}