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 1060 articles , 2 standard articles )

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  1. Brimberg, Jack; Drezner, Zvi: Improved starting solutions for the planar (p)-median problem (2021)
  2. Cavero, Sergio; Pardo, Eduardo G.; Laguna, Manuel; Duarte, Abraham: Multistart search for the cyclic cutwidth minimization problem (2021)
  3. Chang, Jian; Wang, Lifang; Hao, Jin-Kao; Wang, Yang: Parallel iterative solution-based Tabu search for the obnoxious (p)-median problem (2021)
  4. Drezner, Tammy; Drezner, Zvi; Kalczynski, Pawel: Directional approach to gradual cover: the continuous case (2021)
  5. Festa, Paola: Comments on: “Tabu search tutorial. A graph drawing application” (2021)
  6. Glover, Fred; Campos, Vicente; Martí, Rafael: Rejoinder on: “Tabu search tutorial. A graph drawing application” (2021)
  7. Glover, Fred; Campos, Vicente; Martí, Rafael: Tabu search tutorial. A graph drawing application (2021)
  8. Lai, Xiangjing; Hao, Jin-Kao; Fu, Zhang-Hua; Yue, Dong: Neighborhood decomposition based variable neighborhood search and tabu search for maximally diverse grouping (2021)
  9. López-Sánchez, A. D.; Sánchez-Oro, J.; Laguna, M.: A new scatter search design for multiobjective combinatorial optimization with an application to facility location (2021)
  10. Luo, Zhixing; Qin, Hu; Cheng, T. C. E.; Wu, Qinghua; Lim, Andrew: A branch-and-price-and-cut algorithm for the cable-routing problem in solar power plants (2021)
  11. Rao, Yunqing; Wang, Peng; Luo, Qiang: Hybridizing beam search with tabu search for the irregular packing problem (2021)
  12. Zhang, Xindi; Li, Bohan; Cai, Shaowei; Wang, Yiyuan: Efficient local search based on dynamic connectivity maintenance for minimum connected dominating set (2021)
  13. Acharyya, S.; Datta, A. K.: Matching formulation of the staff transfer problem: meta-heuristic approaches (2020)
  14. Alcaraz, Javier; García-Nové, Eva M.; Landete, Mercedes; Monge, Juan F.: The linear ordering problem with clusters: a new partial ranking (2020)
  15. Aribi, Walid Ben; Ammar, Hamadi; Alaya, Mohamed Ben: Stochastic global optimization using tangent minorants for Lipschitz functions (2020)
  16. Brandão, José: A memory-based iterated local search algorithm for the multi-depot open vehicle routing problem (2020)
  17. 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)
  18. Drezner, Tammy; Drezner, Zvi; Kalczynski, Pawel: Directional approach to gradual cover: a maximin objective (2020)
  19. Freitas, Juliana Campos de; Florentino, Helenice de Oliveira; Benedito, Antone dos Santos; Cantane, Daniela Renata: Optimization model applied to radiotherapy planning problem with dose intensity and beam choice (2020)
  20. 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)

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