Tabu search

A user’s guide to tabu search. We describe the main features of tabu search, emphasizing a perspective for guiding a user to understand basic implementation principles for solving combinatorial or nonlinear problems. We also identify recent developments and extensions that have contributed to increasing the efficiency of the method. One of the useful aspects of tabu search is the ability to adapt a rudimentary prototype implementation to encompass additional model elements, such as new types of constraints and objective functions. Similarly, the method itself can be evolved to varying levels of sophistication. We provide several examples of discrete optimization problems to illustrate the strategic concerns of tabu search, and to show how they may be exploited in various contexts. Our presentation is motivated by the emergence of an extensive literature of computational results, which demonstrates that a well-tuned implementation makes it possible to obtain solutions of high quality for difficult problems, yielding outcomes in some settings that have not been matched by other known techniques.

References in zbMATH (referenced in 1038 articles , 2 standard articles )

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  1. Aribi, Walid Ben; Ammar, Hamadi; Alaya, Mohamed Ben: Stochastic global optimization using tangent minorants for Lipschitz functions (2020)
  2. Brandão, José: A memory-based iterated local search algorithm for the multi-depot open vehicle routing problem (2020)
  3. Casado, Silvia; Laguna, Manuel; Pacheco, Joaquín; Puche, Julio C.: Grouping products for the optimization of production processes: a case in the steel manufacturing industry (2020)
  4. Drezner, Tammy; Drezner, Zvi; Kalczynski, Pawel: Directional approach to gradual cover: a maximin objective (2020)
  5. Ibrahim Alsaad, Dhurgam Kalel; Ghanbari, Reza; Sohrabi, Ali Akbar; Ghorbani Moghadam, Khatere: A new model for reassignment of tasks to available employees in Iraq’s firms (2020)
  6. Li, Mingjie; Hao, Jin-Kao; Wu, Qinghua: General swap-based multiple neighborhood adaptive search for the maximum balanced biclique problem (2020)
  7. Pamukcu, Duygu; Balcik, Burcu: A multi-cover routing problem for planning rapid needs assessment under different information-sharing settings (2020)
  8. Pastore, Tommaso; Martínez-Gavara, Anna; Napoletano, Antonio; Festa, Paola; Martí, Rafael: Tabu search for min-max edge crossing in graphs (2020)
  9. Shinitzky, Hilla; Stern, Roni: Batch repair actions for automated troubleshooting (2020)
  10. Waissi, Gary R.; Kaushal, Pragya: A polynomial matrix processing heuristic algorithm for finding high quality feasible solutions for the TSP (2020)
  11. Zhou, Qing; Benlic, Una; Wu, Qinghua: An opposition-based memetic algorithm for the maximum quasi-clique problem (2020)
  12. Andelmin, J.; Bartolini, E.: A multi-start local search heuristic for the green vehicle routing problem based on a multigraph reformulation (2019)
  13. Cravo, G. L.; Amaral, A. R. S.: A GRASP algorithm for solving large-scale single row facility layout problems (2019)
  14. Drezner, Tammy; Drezner, Zvi: Cooperative cover of uniform demand (2019)
  15. Drezner, Tammy; Drezner, Zvi; Kalczynski, Pawel: A directional approach to gradual cover (2019)
  16. Evangelopoulos, Xenophon; Brockmeier, Austin J.; Mu, Tingting; Goulermas, John Y.: Continuation methods for approximate large scale object sequencing (2019)
  17. Fröhlich von Elmbach, Alexander; Scholl, Armin; Walter, Rico: Minimizing the maximal ergonomic burden in intra-hospital patient transportation (2019)
  18. Glover, Fred; Kochenberger, Gary; Du, Yu: Quantum bridge analytics. I: A tutorial on formulating and using QUBO models (2019)
  19. Kononova, P. A.; Kochetov, Yu. A.: A local search algorithm for the single machine scheduling problem with setups and a storage (2019)
  20. Kozin, I. V.; Batovskyi, S. E.: Fragmentary structures in a two-dimensional strip packing problem (2019)

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