To find the global optimum of a given objective function.

To avoid local optima and explore different regions of the search space.

To reduce the computational time required for optimization.

To generate random solutions to a given problem.

A list of solutions that have been visited during the search.

A list of solutions that have been found to be locally optimal.

A list of solutions that have been rejected during the search.

A list of solutions that are considered to be promising and should be explored further.

A condition that allows a move to be made even if it leads to a solution that is already in the tabu list.

A condition that allows a move to be made if it leads to a solution that is better than the current best solution.

A condition that allows a move to be made if it leads to a solution that is within a certain distance from the current best solution.

A condition that allows a move to be made if it leads to a solution that is generated by a different search operator.

A strategy that encourages the search to explore different regions of the search space.

A strategy that prevents the search from getting trapped in local optima.

A strategy that helps the search to converge to the global optimum.

A strategy that reduces the computational time required for optimization.

A strategy that encourages the search to explore different regions of the search space.

A strategy that prevents the search from getting trapped in local optima.

A strategy that helps the search to converge to the global optimum.

A strategy that reduces the computational time required for optimization.

Scheduling problems

Traveling salesman problem

Graph coloring problems

Linear programming problems

It is guaranteed to find the global optimum of a given objective function.

It is able to avoid local optima and explore different regions of the search space.

It is computationally more efficient than other local search techniques.

It is easy to implement and requires minimal tuning of parameters.

It is guaranteed to find the global optimum of a given objective function.

It is able to avoid local optima and explore different regions of the search space.

It is computationally more efficient than other local search techniques.

It is difficult to implement and requires extensive tuning of parameters.

Tabu list size

Aspiration criterion

Diversification strategy

Learning rate

A fixed number of iterations

A maximum number of consecutive iterations without improvement

A time limit

All of the above

Adaptive Tabu Search

Reactive Tabu Search

Deterministic Tabu Search

Simulated Annealing

Logistics and transportation

Scheduling and resource allocation

Financial optimization

Medical diagnosis

Fred Glover

David Johnson

Eugene Lawler

Richard Karp

1970s

1980s

1990s

2000s

Polynomial

NP-hard

NP-complete

Undecidable