MATLAB Implementation of Simulated Annealing Algorithm

The MATLAB source code for the simulated annealing algorithm represents a widely-used optimization method particularly effective for analyzing and processing network traffic data. This implementation enables identification of optimal solutions within network traffic datasets, facilitating deeper understanding and analysis of network flow patterns. The algorithm is implemented in MATLAB, utilizing its computational efficiency and matrix operations for simplified execution and practical application. Simulated annealing is a stochastic search algorithm inspired by the metallurgical annealing process, where controlled cooling helps find global optimum solutions while avoiding local minima. The MATLAB code typically includes key components such as: - Temperature scheduling function controlling the cooling rate - Energy calculation method evaluating solution quality - Neighbor solution generation mechanism for exploration - Acceptance probability function using Boltzmann distribution This flexible approach can address various optimization challenges, including network traffic analysis where it helps identify patterns, optimize routing, and manage bandwidth allocation. Through this algorithm implementation, network traffic data can be processed more effectively to extract valuable insights, support accurate predictions, and inform data-driven decisions. The source code provides a practical foundation for researchers and engineers working with network traffic analysis and optimization problems. The implementation likely includes main functions like `simulated_annealing()`, `objective_function()`, and `temperature_update()`, with parameters adjustable for specific network traffic characteristics such as data volume, flow patterns, and optimization objectives. This structured approach ensures the algorithm can adapt to different network scenarios while maintaining computational efficiency.