MATLAB Code Implementation of Simulated Annealing Toolbox

Simulated Annealing (SA) is a probabilistic global optimization algorithm inspired by atomic thermal movements during metal annealing processes. In the MATLAB environment, the SATools toolbox provides a streamlined implementation framework for this algorithm. This toolbox is specifically designed to simplify SA implementation workflows, making it particularly effective for solving complex nonlinear optimization problems with strong performance in high-dimensional, multimodal function optimization scenarios.

The core mechanism of SATools revolves around temperature parameter control to regulate search scope: at high temperatures, the algorithm accepts suboptimal solutions to escape local optima, while gradually converging to high-quality solutions as temperature decreases. Users simply need to define three key components - the objective function (typically implemented as a MATLAB function file), initial solution vector, and annealing parameters (including initial temperature, cooling rate, and iteration limits). The toolbox automatically handles the iterative optimization process through its main sa_optimize() function, which manages the metropolis criterion evaluation and solution acceptance probability calculations, ultimately returning the optimal solution and its fitness value.

In practical applications, this toolbox seamlessly integrates with MATLAB workflows for engineering optimization, machine learning hyperparameter tuning, and path planning scenarios. Its advantages include highly interpretable parameters and built-in visualization interfaces that allow users to monitor convergence behavior through plots of temperature decay and fitness improvement over iterations. For AI researchers and developers, this plug-and-play tool significantly reduces implementation barriers by handling the algorithmic complexities through predefined classes and methods, allowing focus on problem modeling rather than low-level coding. The toolbox architecture supports custom cooling schedules and neighborhood generation functions through modular design, enabling adaptations for specific problem domains.