MATLAB Implementation of Taboo Search Algorithm for Function Optimization

The taboo search algorithm in MATLAB is a powerful method commonly used for function optimization and finding optimal values. This algorithm operates as an iterative optimization technique that intelligently restricts certain search directions to prevent entrapment in local optima. The core implementation involves maintaining a taboo list (or prohibition table) that records recently visited solutions or moves, preventing the algorithm from cycling back to previously explored states. Key implementation aspects include: - Initialization of solution parameters and taboo list structure - Neighborhood generation functions to explore adjacent solutions - Evaluation of objective function values for candidate solutions - Dynamic adjustment of taboo tenure and aspiration criteria - Adaptive parameter tuning during the search process The algorithm demonstrates significant adaptability through its ability to dynamically modify taboo parameters during execution, thereby enhancing search efficiency. Typical MATLAB implementation would involve functions for solution representation, fitness evaluation, and taboo list management. The taboo search algorithm proves particularly effective for complex optimization landscapes where traditional methods might prematurely converge to suboptimal solutions. This approach provides a robust framework for addressing challenging function optimization problems and locating global optima.