Simulated Annealing Algorithm

In this article, we provide a detailed introduction to the simulated annealing algorithm along with precise MATLAB implementation code. Simulated annealing is a widely-used optimization algorithm that mimics the physical process of metal cooling from high temperatures. By simulating this thermal dynamics process, the algorithm can escape local optima and converge toward global optimal solutions through controlled probability-based acceptance criteria. The MATLAB implementation typically includes key components such as: temperature initialization, neighbor solution generation, energy difference calculation, and the Metropolis acceptance criterion with exponential probability functions. The algorithm's parameters - including initial temperature, cooling schedule, and iteration settings - can be customized for specific optimization problems. In practical applications, simulated annealing has been extensively adopted in logistics planning, power system dispatch, image processing, and machine learning domains. Understanding the algorithm's principles and implementation techniques is crucial for effectively applying it to real-world problems. This detailed guide will help you grasp the core concepts and apply the algorithm successfully to your optimization challenges.