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 1018 articles , 2 standard articles )

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  1. Pastore, Tommaso; Martínez-Gavara, Anna; Napoletano, Antonio; Festa, Paola; Martí, Rafael: Tabu search for min-max edge crossing in graphs (2020)
  2. Andelmin, J.; Bartolini, E.: A multi-start local search heuristic for the green vehicle routing problem based on a multigraph reformulation (2019)
  3. Cravo, G. L.; Amaral, A. R. S.: A GRASP algorithm for solving large-scale single row facility layout problems (2019)
  4. Drezner, Tammy; Drezner, Zvi; Kalczynski, Pawel: A directional approach to gradual cover (2019)
  5. Evangelopoulos, Xenophon; Brockmeier, Austin J.; Mu, Tingting; Goulermas, John Y.: Continuation methods for approximate large scale object sequencing (2019)
  6. Fröhlich von Elmbach, Alexander; Scholl, Armin; Walter, Rico: Minimizing the maximal ergonomic burden in intra-hospital patient transportation (2019)
  7. Glover, Fred; Kochenberger, Gary; Du, Yu: Quantum bridge analytics. I: A tutorial on formulating and using QUBO models (2019)
  8. Lai, Xiangjing; Hao, Jin-Kao; Yue, Dong: Two-stage solution-based tabu search for the multidemand multidimensional knapsack problem (2019)
  9. Lu, Zhi; Hao, Jin-Kao; Zhou, Yi: Stagnation-aware breakout tabu search for the minimum conductance graph partitioning problem (2019)
  10. Mehrdoost, Zahra: Unstructured grid adaptation for multiscale finite volume method (2019)
  11. Scutari, Marco; Vitolo, Claudia; Tucker, Allan: Learning Bayesian networks from big data with greedy search: computational complexity and efficient implementation (2019)
  12. Semenov, Alexander A.: Merging variables: one technique of search in pseudo-Boolean optimization (2019)
  13. Shang, Zhen; Zhao, Songzheng; Hao, Jin-Kao; Yang, Xue; Ma, Fuda: Multiple phase tabu search for bipartite Boolean quadratic programming with partitioned variables (2019)
  14. Stefanello, Fernando; Aggarwal, Vaneet; Buriol, Luciana S.; Resende, Mauricio G. C.: Hybrid algorithms for placement of virtual machines across geo-separated data centers (2019)
  15. Thevenin, Simon; Zufferey, Nicolas: Learning variable neighborhood search for a scheduling problem with time windows and rejections (2019)
  16. Vié, Marie-Sklaerder; Zufferey, Nicolas; Cordeau, Jean-François: Solving the wire-harness design problem at a European car manufacturer (2019)
  17. Zamani, Reza: An innovative four-layer heuristic for scheduling multi-mode projects under multiple resource constrains (2019)
  18. Zhou, Qing; Benlic, Una; Wu, Qinghua; Hao, Jin-Kao: Heuristic search to the capacitated clustering problem (2019)
  19. Archetti, Claudia; Fernández, Elena; Huerta-Muñoz, Diana L.: A two-phase solution algorithm for the flexible periodic vehicle routing problem (2018)
  20. Bai, Ruibin; Woodward, John R.; Subramanian, Nachiappan; Cartlidge, John: Optimisation of transportation service network using (\kappa)-node large neighbourhood search (2018)

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