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 895 articles , 2 standard articles )

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  1. Battistutta, Michele; Schaerf, Andrea; Urli, Tommaso: Feature-based tuning of single-stage simulated annealing for examination timetabling (2017)
  2. Galligari, Alessandro; Maischberger, Mirko; Schoen, Fabio: Local search heuristics for the zone planning problem (2017)
  3. Ma, Fuda; Hao, Jin-Kao: A multiple search operator heuristic for the max-k-cut problem (2017)
  4. Martínez-Gavara, Anna; Campos, Vicente; Laguna, Manuel; Martí, Rafael: Heuristic solution approaches for the maximum minsum dispersion problem (2017)
  5. Sharma, Amita; Utz, Sebastian; Mehra, Aparna: Omega-CVaR portfolio optimization and its worst case analysis (2017)
  6. Vömel, Christof; de Lorenzi, Flavio; Beer, Samuel; Fuchs, Erwin: The secret life of keys: on the calculation of mechanical lock systems (2017)
  7. Zakouni, Amiyne; Luo, Jiawei; Kharroubi, Fouad: Genetic algorithm and tabu search algorithm for solving the static manycast RWA problem in optical networks (2017)
  8. Zyngier, Danielle: An uncertainty management framework for industrial applications (2017)
  9. Amaral, Paula; Pais, Tiago Cardal: Compromise ratio with weighting functions in a tabu search multi-criteria approach to examination timetabling (2016)
  10. Amaran, Satyajith; Sahinidis, Nikolaos V.; Sharda, Bikram; Bury, Scott J.: Simulation optimization: a review of algorithms and applications (2016)
  11. Cheng, T.C.Edwin; Peng, Bo; Lü, Zhipeng: A hybrid evolutionary algorithm to solve the job shop scheduling problem (2016)
  12. Chen, Yuning; Hao, Jin-Kao; Glover, Fred: A hybrid metaheuristic approach for the capacitated arc routing problem (2016)
  13. Cortinhal, Maria João; Mourão, Maria C^andida; Nunes, Ana Catarina: Local search heuristics for sectoring routing in a household waste collection context (2016)
  14. De Santis, M.; Festa, P.; Liuzzi, G.; Lucidi, S.; Rinaldi, F.: A nonmonotone GRASP (2016)
  15. Ferrucci, Francesco; Bock, Stefan: Pro-active real-time routing in applications with multiple request patterns (2016)
  16. Gach, Olivier; Hao, Jin-Kao: Combined neighborhood tabu search for community detection in complex networks (2016)
  17. Goerigk, Marc; Westphal, Stephan: A combined local search and integer programming approach to the traveling tournament problem (2016)
  18. Holm, Åsa; Carlsson Tedgren, Åsa; Larsson, Torbjörn: Heuristics for integrated optimization of catheter positioning and Dwell time distribution in prostate HDR brachytherapy (2016)
  19. Khalid, Ruzelan; Nawawi, Mohd.Kamal Mohd.; Kawsar, Luthful A.; Ghani, Noraida A.; Kamil, Anton A.; Mustafa, Adli: The evaluation of pedestrians’ behavior using $M/G/C/C$ analytical, weighted distance and real distance simulation models (2016)
  20. Lai, Xiangjing; Hao, Jin-Kao: A tabu search based memetic algorithm for the Max-Mean dispersion problem (2016)

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