Parameter Estimation and Optimization with Simulated Annealing in MATLAB

Finding global minima in complex, non-linear engineering models is incredibly challenging when traditional gradient-based methods get stuck in local traps. This text-only course guides you through the fundamentals of global optimization, focusing on the powerful simulated annealing algorithm. You will transition from understanding basic thermodynamic analogies to writing robust parameter estimation routines that handle noisy, real-world engineering data. What you'll learn: - Learn the core principles of simulated annealing and how it avoids local minima. - Configure cooling schedules, acceptance probabilities, and neighbor selection strategies. - Apply global optimization techniques to solve complex parameter estimation problems. - Understand Bayesian statistics basics to incorporate prior knowledge into your models. - Practice writing and debugging optimization scripts in MATLAB. - Implement modern best practices for verifying algorithm convergence and sensitivity. You will start with foundational definitions of optimization and probability before moving step-by-step through algorithm design, parameter fitting, and Bayesian analysis using clear, written explanations and structured code examples. This course is designed for beginners to numerical optimization, engineering students, and researchers. No prior experience with heuristic optimization is required, though a basic familiarity with MATLAB is helpful. Start mastering global optimization and parameter estimation today.