Implementation of Tabu Search Optimization Algorithm

Tabu Search is a metaheuristic optimization algorithm primarily used for solving combinatorial optimization problems. It incorporates a short-term memory mechanism to prevent the search process from getting trapped in local optima while effectively exploring the solution space.

The core concept of Tabu Search involves maintaining a Tabu List that records recently visited solutions or move operations. When the algorithm encounters these "tabu" solutions or operations during the search process, it actively avoids them, thereby forcing the exploration of new regions. This mechanism enables the algorithm to escape local optima and continue searching for better solutions.

In MATLAB implementation, the algorithm typically includes the following key components: 1) Initial solution generation strategy (often using random initialization or greedy approaches); 2) Neighborhood structure definition (specifying how to generate adjacent solutions from the current solution); 3) Tabu list management (implementing FIFO queue with fixed size or adaptive length); 4) Solution evaluation function (objective function calculation); 5) Termination condition checking (maximum iterations, convergence criteria, or time limits).

The algorithm workflow generally proceeds as follows: Starting from an initial solution, it searches for the optimal candidate solution in the current neighborhood that is not tabu, even if this solution may be worse than the current solution (this is known as the "hill-climbing" strategy). Then it updates the Tabu List, records the characteristics of this move or solution, and sets these characteristics as unusable for a certain number of iterations.

Notably, Tabu Search also incorporates "Aspiration Criteria" - when a tabu move can yield a solution better than the current best solution, the taboo restriction can be broken to accept this move. This flexibility is crucial for the algorithm's efficiency. In MATLAB code, this is typically implemented as an if-condition that overrides tabu status when aspiration conditions are met.

In practical applications, MATLAB-implemented Tabu Search can be effectively combined with other optimization algorithms (such as genetic algorithms). For example, one can first use a genetic algorithm to generate a set of good initial solutions, then apply Tabu Search for refined local search. This hybrid strategy often achieves better optimization results by leveraging the global exploration capabilities of genetic algorithms with the local intensification power of Tabu Search.