Implementation of Simulated Annealing Algorithm with MATLAB Code

This MATLAB implementation provides a robust and user-friendly simulated annealing algorithm program designed for solving various optimization challenges. The code features temperature scheduling with geometric cooling, Metropolis criterion acceptance for worse solutions, and configurable stopping conditions. Key functions include objective function customization, neighbor solution generation, and progress visualization through convergence plots. The program is particularly effective for combinatorial optimization, global search, and multi-modal function optimization problems. Its modular structure allows easy adaptation for specific requirements - users can modify the annealing schedule, solution representation, or energy calculation method. The implementation includes detailed comments explaining the algorithm's three main components: temperature decay (using exponential reduction), state transition probability calculation, and termination criteria evaluation. Whether you're a student learning metaheuristic algorithms or a professional solving complex optimization problems, this implementation serves as both an educational tool and practical solution. The code supports parameter tuning through accessible configuration variables and provides real-time optimization progress tracking. Its flexibility enables extensions for constrained optimization or hybrid algorithms combining simulated annealing with local search techniques. Download now to explore this versatile optimization tool that demonstrates fundamental simulated annealing concepts while maintaining production-ready code quality!