MATLAB Implementation of Simulated Annealing Algorithm with Case Studies

Simulated annealing is an optimization algorithm capable of solving various complex problems. The implementation code can be analyzed and improved based on specific cases to meet different requirements. This algorithm employs a stochastic search approach that mimics the physical process of metal annealing, progressively adjusting parameters to converge toward optimal solutions through controlled temperature reduction and probabilistic acceptance criteria. In practical applications, the simulated annealing algorithm has been extensively utilized in combinatorial optimization, traveling salesman problems, graph coloring, and other NP-hard domains. The core implementation typically involves: 1) designing an appropriate cooling schedule using exponential or logarithmic temperature decay functions, 2) defining neighborhood structures for solution perturbation, and 3) implementing Metropolis acceptance criteria using probability functions like exp(-ΔE/T). Understanding and mastering the MATLAB implementation code—including key functions such as temperature update, energy calculation, and solution acceptance logic—holds significant importance for solving real-world optimization challenges. The code architecture allows modular modifications where users can customize objective functions, constraint handling methods, and termination conditions based on specific problem characteristics.