MATLAB Code Implementation of Simulated Annealing Algorithm

This article discusses a highly powerful optimization technique—the Simulated Annealing algorithm. Below you will find several practical examples that demonstrate different implementations and applications of this algorithm. These examples include code structures for handling temperature schedules, neighbor generation functions, and energy calculation methods commonly used in optimization problems. The implementation showcases key components like the Metropolis criterion for acceptance probabilities and cooling schedule configurations. To execute these examples, follow these steps: 1. Download and extract the example code files containing MATLAB scripts (.m files) and supporting functions. 2. Launch MATLAB software on your system. 3. In the MATLAB command window, navigate to the directory containing the extracted code files using the cd command or current folder browser. 4. Run the appropriate MATLAB scripts (e.g., main_sa.m or example1.m) to observe the algorithm's iteration process, convergence behavior, and optimization results. The code includes visualization functions to plot convergence curves and solution improvements over iterations. These examples demonstrate practical implementations of simulated annealing for solving combinatorial optimization problems, continuous parameter optimization, and constraint handling techniques. The code contains adjustable parameters for annealing schedules, neighborhood search strategies, and cost function implementations. Should you have any questions or require further technical assistance regarding the algorithm implementation or code customization, please feel free to seek additional guidance.