Editing Tabu search (section)

==Types of memory==
The memory structures used in tabu search can roughly be divided into three categories:<ref name="glover90b">{{cite journal
 | author = Fred Glover
 | year = 1990
 | title = Tabu Search: A Tutorial
 | journal = Interfaces
 }}
</ref>
* Short-term: The list of solutions recently considered. If a potential solution appears on the tabu list, it cannot be revisited until it reaches an expiration point.
* Intermediate-term: Intensification rules intended to bias the search towards promising areas of the search space.
* Long-term: Diversification rules that drive the search into new regions (i.e., regarding resets when the search becomes stuck in a plateau or a suboptimal dead-end).

Short-term, intermediate-term and long-term memories can overlap in practice. Within these categories, memory can further be differentiated by measures such as frequency and impact of changes made. One example of an intermediate-term memory structure is one that prohibits or encourages solutions that contain certain attributes (e.g., solutions that include undesirable or desirable values for certain variables) or a memory structure that prevents or induces certain moves (e.g. based on frequency memory applied to solutions sharing features in common with unattractive or attractive solutions found in the past). In short-term memory, selected attributes in solutions recently visited are labelled "tabu-active." Solutions that contain tabu-active elements are banned. Aspiration criteria are employed to override a solution's tabu state, thereby including the otherwise-excluded solution in the allowed set (provided the solution is “good enough” according to a measure of quality or diversity). A simple and commonly used aspiration criterion is to allow solutions which are better than the currently-known best solution.

Short-term memory alone may be enough to achieve solutions superior to those found by conventional local search methods, but intermediate and long-term structures are often necessary for solving harder problems.<ref name="malek89">{{cite journal
 |author1=M. Malek |author2=M. Huruswamy |author3=H. Owens |author4=M. Pandya | year = 1989
 | title = Serial and parallel search techniques for the traveling salesman problem
 | journal = Annals of OR: Linkages with Artificial Intelligence
 }}</ref>  Tabu search is often benchmarked against other [[metaheuristic]] methods — such as [[simulated annealing]], [[genetic algorithm]]s, [[ant colony optimization algorithms]], reactive search optimization, [[Guided Local Search|guided local search]], or [[greedy randomized adaptive search procedure|greedy randomized adaptive search]]. In addition, tabu search is sometimes combined with other metaheuristics to create hybrid methods. The most common tabu search hybrid arises by joining TS with [[scatter search]],<ref name="glover2000">{{cite journal
 |author1=F. Glover, M. Laguna  |author2=R. Marti
  |name-list-style=amp | year = 2000
 | title = Fundamentals of Scatter Search and Path Relinking
 | journal = Control and Cybernetics
 | volume = 29
 | issue = 3
 | pages = 653–684
 }}</ref><ref name="laguna2003">{{cite book
 |author1=M. Laguna  |author2=R. Marti
  |name-list-style=amp | year = 2003
 | title = Scatter Search: Methodology and Implementations in C 
 |publisher = Kluwer Academic Publishers
 |isbn=9781402073762
 }}</ref> a class of population-based procedures which has roots in common with tabu search, and is often employed in solving large non-linear optimization problems.