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 842 articles , 1 standard article )

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  1. Amaran, Satyajith; Sahinidis, Nikolaos V.; Sharda, Bikram; Bury, Scott J.: Simulation optimization: a review of algorithms and applications (2016)
  2. Cheng, T.C.Edwin; Peng, Bo; Lü, Zhipeng: A hybrid evolutionary algorithm to solve the job shop scheduling problem (2016)
  3. De Santis, M.; Festa, P.; Liuzzi, G.; Lucidi, S.; Rinaldi, F.: A nonmonotone GRASP (2016)
  4. Gach, Olivier; Hao, Jin-Kao: Combined neighborhood tabu search for community detection in complex networks (2016)
  5. Goerigk, Marc; Westphal, Stephan: A combined local search and integer programming approach to the traveling tournament problem (2016)
  6. Holm, Åsa; Carlsson Tedgren, Åsa; Larsson, Torbjörn: Heuristics for integrated optimization of catheter positioning and Dwell time distribution in prostate HDR brachytherapy (2016)
  7. Pillay, Nelishia: A review of hyper-heuristics for educational timetabling (2016)
  8. Poppenborg, Jens; Knust, Sigrid: A flow-based tabu search algorithm for the RCPSP with transfer times (2016)
  9. Wang, Yang; Hao, Jin-Kao; Glover, Fred; Lü, Zhipeng; Wu, Qinghua: Solving the maximum vertex weight clique problem via binary quadratic programming (2016)
  10. Zhou, Yi; Hao, Jin-Kao; Goëffon, Adrien: A three-phased local search approach for the clique partitioning problem (2016)
  11. Aringhieri, Roberto; Cordone, Roberto; Grosso, Andrea: Construction and improvement algorithms for dispersion problems (2015)
  12. Ashik Mathew, K.; Östergård, Patric R.J.: On hypercube packings, blocking sets and a covering problem (2015)
  13. Bragin, Mikhail A.; Luh, Peter B.; Yan, Joseph H.; Yu, Nanpeng; Stern, Gary A.: Convergence of the surrogate Lagrangian relaxation method (2015)
  14. Cerqueti, Roy; Falbo, Paolo; Guastaroba, Gianfranco; Pelizzari, Cristian: Approximating multivariate Markov chains for bootstrapping through contiguous partitions (2015)
  15. Drezner, Tammy; Drezner, Zvi; Kalczynski, Pawel: A leader-follower model for discrete competitive facility location (2015)
  16. Gevezes, Theodoros; Pitsoulis, Leonidas: A greedy randomized adaptive search procedure with path relinking for the shortest superstring problem (2015)
  17. Iellamo, Stefano; Alekseeva, Ekaterina; Chen, Lin; Coupechoux, Marceau; Kochetov, Yuri: Competitive location in cognitive radio networks (2015)
  18. Keshavarz, Taha; Salmasi, Nasser; Varmazyar, Mohsen: Minimizing total completion time in the flexible flowshop sequence-dependent group scheduling problem (2015)
  19. Kharroubi, Fouad; He, Jing; Tang, Jin; Chen, Ming; Chen, Lin: Evaluation performance of genetic algorithm and tabu search algorithm for solving the MAX-RWA problem in all-optical networks (2015)
  20. Koohestani, Behrooz; Poli, Riccardo: Addressing the envelope reduction of sparse matrices using a genetic programming system (2015)

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