11 Facts About Tabu search

Tabu search is a metaheuristic search method employing local search methods used for mathematical optimization.

Local Tabu search methods have a tendency to become stuck in suboptimal regions or on plateaus where many solutions are equally fit.

Implementation of tabu search uses memory structures that describe the visited solutions or user-provided sets of rules.

Tabu search is a metaheuristic algorithm that can be used for solving combinatorial optimization problems.

In recent years, journals in a wide variety of fields have published tutorial articles and computational studies documenting successes by tabu search in extending the frontier of problems that can be handled effectively — yielding solutions whose quality often significantly surpasses that obtained by methods previously applied.

Tabu search uses a local or neighborhood search procedure to iteratively move from one potential solution to an improved solution in the neighborhood of, until some stopping criterion has been satisfied.

Tabu search has several similarities with simulated annealing, as both involve possible down hills moves.

Tabu search is often benchmarked against other metaheuristic methods — such as Simulated annealing, genetic algorithms, Ant colony optimization algorithms, Reactive search optimization, Guided Local Search, or greedy randomized adaptive search.

The most common tabu search hybrid arises by joining TS with Scatter Search, 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.

The Tabu search starts with an initial solution, which can be generated randomly or according to some sort of nearest neighbor algorithm.

Once the simple tabu search stops, it returns the best solution found during its execution.