MATLAB Code Implementation of Simulated Annealing Algorithm

Simulated Annealing Algorithm is a widely used numerical optimization method particularly suitable for beginners due to its straightforward implementation and intuitive comprehension. This algorithm helps solve complex problems and identify optimal solutions by simulating the physical process of metal annealing. The core principle involves exploring the solution space through probabilistic acceptance of random solutions while gradually reducing the "temperature" parameter to enhance local search intensity. In MATLAB implementation, key components typically include: 1) Initial solution generation using random initialization functions like rand() or randi(), 2) Temperature scheduling through geometric decay (e.g., T = T * cooling_rate), 3) Neighbor solution generation via perturbation functions, and 4) Acceptance probability calculation using exp(-ΔE/T) based on energy difference. The algorithm finds extensive applications across engineering, computer science, and operations research domains. Therefore, learning and mastering simulated annealing algorithm holds significant importance for beginners in computational optimization.