LKH is an effective implementation of the Lin-Kernighan heuristic for solving the traveling salesman problem. Computational experiments have shown that LKH is highly effective. Even though the algorithm is approximate, optimal solutions are produced with an impressively high frequency. LKH has produced optimal solutions for all solved problems we have been able to obtain; including a 85,900-city instance (at the time of writing, the largest nontrivial instance solved to optimality). Furthermore, the algorithm has improved the best known solutions for a series of large-scale instances with unknown optima, among these a 1,904,711-city instance (World TSP).

References in zbMATH (referenced in 73 articles )

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  1. Chassein, André; Goerigk, Marc: On the recoverable robust traveling salesman problem (2016)
  2. Fages, Jean-Guillaume; Lorca, Xavier; Rousseau, Louis-Martin: The salesman and the tree: the importance of search in CP (2016)
  3. Wang, Xingyin; Golden, Bruce; Wasil, Edward; Zhang, Rui: The min-max split delivery multi-depot vehicle routing problem with minimum service time requirement (2016)
  4. Wang, Yong; Remmel, Jeffrey B.: A binomial distribution model for the traveling salesman problem based on frequency quadrilaterals (2016)
  5. Weise, Thomas; Wu, Yuezhong; Chiong, Raymond; Tang, Ke; Lässig, Jörg: Global versus local search: the impact of population sizes on evolutionary algorithm performance (2016)
  6. Cordeau, Jean-François; Laganà, Demetrio; Musmanno, Roberto; Vocaturo, Francesca: A decomposition-based heuristic for the multiple-product inventory-routing problem (2015)
  7. Goldengorin, B.I.; Malyshev, D.S.; Pardalos, P.M.; Zamaraev, V.A.: A tolerance-based heuristic approach for the weighted independent set problem (2015)
  8. Hoos, Holger H.; Stützle, Thomas: On the empirical time complexity of finding optimal solutions vs proving optimality for Euclidean TSP instances (2015)
  9. Talarico, Luca; Meisel, Frank; Sörensen, Kenneth: Ambulance routing for disaster response with patient groups (2015)
  10. Yu, Vincent F.; Lin, Shin-Yu: A simulated annealing heuristic for the open location-routing problem (2015)
  11. Baniasadi, Pouya; Ejov, Vladimir; Filar, Jerzy A.; Haythorpe, Michael; Rossomakhine, Serguei: Deterministic “snakes and ladders” heuristic for the Hamiltonian cycle problem (2014)
  12. Dowlatshahi, Mohammad Bagher; Nezamabadi-pour, Hossein; Mashinchi, Mashaallah: A discrete gravitational search algorithm for solving combinatorial optimization problems (2014)
  13. Fischer, A.; Fischer, F.; Jäger, G.; Keilwagen, J.; Molitor, P.; Grosse, I.: Exact algorithms and heuristics for the quadratic traveling salesman problem with an application in bioinformatics (2014)
  14. Florios, Kostas; Mavrotas, George: Generation of the exact Pareto set in multi-objective traveling salesman and set covering problems (2014)
  15. Hutter, Frank; Xu, Lin; Hoos, Holger H.; Leyton-Brown, Kevin: Algorithm runtime prediction: methods & evaluation (2014)
  16. LaRusic, John; Punnen, Abraham P.: The asymmetric bottleneck traveling salesman problem: algorithms, complexity and empirical analysis (2014)
  17. Matusiak, Marek; de Koster, René; Kroon, Leo; Saarinen, Jari: A fast simulated annealing method for batching precedence-constrained customer orders in a warehouse (2014)
  18. Feng, Xiang; Lau, Francis C.M.; Yu, Huiqun: A novel bio-inspired approach based on the behavior of mosquitoes (2013)
  19. Benchimol, Pascal; van Hoeve, Willem-Jan; Régin, Jean-Charles; Rousseau, Louis-Martin; Rueher, Michel: Improved filtering for weighted circuit constraints (2012)
  20. Chistyakov, Vyacheslav V.; Goldengorin, Boris I.; Pardalos, Panos M.: Extremal values of global tolerances in combinatorial optimization with an additive objective function (2012)

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