MATLAB-based Simulated Annealing Algorithm Toolbox

This document introduces a MATLAB-based simulated annealing algorithm toolbox designed for solving optimization problems. The toolbox implements simulated annealing, a heuristic optimization algorithm that mimics the metallurgical annealing process by performing stochastic searches in solution space while gradually reducing temperature parameters to converge toward optimal solutions. The toolbox provides a collection of functions and utilities that enable users to easily implement simulated annealing algorithms. Key components include temperature scheduling functions (exponential, logarithmic, or custom decay patterns), solution perturbation methods for neighborhood exploration, and acceptance probability calculators following the Metropolis criterion. These modular components allow researchers to customize the algorithm behavior for specific problem domains. Developed natively for MATLAB, the toolbox integrates seamlessly with MATLAB's optimization environment, supporting matrix operations and visualization capabilities. Users can implement objective functions using standard MATLAB syntax and leverage built-in plotting functions to monitor convergence behavior. The implementation includes both continuous and discrete variable optimization support, with specialized functions for handling constraints through penalty methods or feasible solution maintenance. For academic research and engineering applications, this toolbox offers robust optimization capabilities with configurable parameters including initial temperature, cooling rate, iteration limits, and stopping criteria. The package includes demonstration scripts showcasing applications in traveling salesman problems, function minimization, and parameter estimation tasks, providing practical examples of algorithm implementation and performance tuning.