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 931 articles , 2 standard articles )

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  1. Ben Jouida, Sihem; Krichen, Saoussen: A DSS based on optimizer tools and MTS meta-heuristic for the warehousing problem with conflicts (2018)
  2. Chen, Yuning; Hao, Jin-Kao: Two phased hybrid local search for the periodic capacitated arc routing problem (2018)
  3. Noham, Reut; Tzur, Michal: Designing humanitarian supply chains by incorporating actual post-disaster decisions (2018)
  4. Sioud, A.; Gagné, C.: Enhanced migrating birds optimization algorithm for the permutation flow shop problem with sequence dependent setup times (2018)
  5. Battistutta, Michele; Schaerf, Andrea; Urli, Tommaso: Feature-based tuning of single-stage simulated annealing for examination timetabling (2017)
  6. Bueno, Marcos L.P.; Hommersom, Arjen; Lucas, Peter J.F.; Linard, Alexis: Asymmetric hidden Markov models (2017)
  7. Bürgy, Reinhard: A neighborhood for complex job shop scheduling problems with regular objectives (2017)
  8. Cabrera-Guerrero, Guillermo; Lagos, Carolina; Castañeda, Carolina; Johnson, Franklin; Paredes, Fernando; Cabrera, Enrique: Parameter tuning for local-search-based matheuristic methods (2017)
  9. Galligari, Alessandro; Maischberger, Mirko; Schoen, Fabio: Local search heuristics for the zone planning problem (2017)
  10. Glover, Fred; Rego, César: New relationships for multi-neighborhood search for the minimum linear arrangement problem (2017)
  11. Hanafi, Saïd; Todosijević, Raca: Mathematical programming based heuristics for the 0--1 MIP: a survey (2017)
  12. Jayaswal, Sachin; Vidyarthi, Navneet; Das, Sagnik: A cutting plane approach to combinatorial bandwidth packing problem with queuing delays (2017)
  13. Ma, Fuda; Hao, Jin-Kao: A multiple search operator heuristic for the max-k-cut problem (2017)
  14. Martínez-Gavara, Anna; Campos, Vicente; Laguna, Manuel; Martí, Rafael: Heuristic solution approaches for the maximum minsum dispersion problem (2017)
  15. Rybarczyk, Agnieszka; Hertz, Alain; Kasprzak, Marta; Blazewicz, Jacek: Tabu search for the RNA partial degradation problem (2017)
  16. Salem, Mariem Ben; Hanafi, Saïd; Taktak, Raouia; Abdallah, Han^ene Ben: Probabilistic tabu search with multiple neighborhoods for the Disjunctively Constrained Knapsack Problem (2017)
  17. Sánchez-Oro, Jesús; Martínez-Gavara, Anna; Laguna, Manuel; Martí, Rafael; Duarte, Abraham: Variable neighborhood scatter search for the incremental graph drawing problem (2017)
  18. Sergienko, I. V.; Shylo, V. P.: Kernel technology to solve discrete optimization problems (2017)
  19. Sharma, Amita; Utz, Sebastian; Mehra, Aparna: Omega-CVaR portfolio optimization and its worst case analysis (2017)
  20. Vömel, Christof; de Lorenzi, Flavio; Beer, Samuel; Fuchs, Erwin: The secret life of keys: on the calculation of mechanical lock systems (2017)

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