MATLAB Implementation of Tabu Search Algorithm

Tabu Search is a heuristic algorithm used for solving optimization problems, particularly effective in finding global optima for complex functions. MATLAB, as a powerful numerical computing tool, is well-suited for implementing such intelligent optimization algorithms. The core concept of Tabu Search involves maintaining a "tabu list" to avoid getting trapped in local optima. The algorithm records recently visited solutions or move directions during the search process and prohibits revisiting them for a certain number of iterations, thereby forcing exploration of new regions. This strategy effectively helps escape local optimal traps. Implementing Tabu Search in MATLAB typically involves the following key steps: First, define the objective function and neighborhood structure - where the neighborhood determines how candidate solutions are generated from the current solution. In MATLAB code, this is often implemented using anonymous functions (@) for objective definitions and array operations for neighborhood generation. Second, set the tabu list size and tabu tenure parameters, which directly impact the algorithm's exploration capability. For each iteration, the algorithm evaluates all non-tabu solutions within the neighborhood using vectorized operations for efficiency, selects the optimal solution to update the current state, and updates the tabu list using MATLAB's array management features. Compared to traditional optimization algorithms, Tabu Search's advantage lies in its ability to handle complex optimization problems with non-convex, multi-modal characteristics. MATLAB's matrix operation capabilities enable efficient batch evaluation of neighborhood solutions through vectorization, while its flexible array structures facilitate effective tabu list management. Typical applications include engineering parameter optimization, path planning, production scheduling, and other domains where complex optimization is required. During implementation, parameter tuning requires attention - a tabu list size that's too small may cause cycling, while an excessively large size reduces efficiency. In MATLAB implementations, this can be optimized using experimental parameter sweeps. Incorporating appropriate random perturbation strategies (such as long-term memory mechanisms) implemented through random number generation functions (rand, randn) can further enhance global search capabilities.