Stochastic Search and Optimization Methods

This paper presents methodologies for stochastic search and optimization, covering random search techniques, recursive least squares algorithms, and stochastic optimization approaches. These methods are applicable to various practical scenarios such as model parameter calibration and optimal solution discovery. The implementation typically involves generating random candidate solutions, evaluating objective functions, and iteratively refining parameters using stochastic gradients or population-based evolution. For complex nonlinear function optimization, the text demonstrates how to apply adaptive step-size control and convergence criteria in algorithm design. Practical application cases are provided as references, showcasing implementation details like parameter initialization strategies and termination conditions. Based on this foundation, readers can deepen their understanding of stochastic search concepts and flexibly apply these methods using programming frameworks that incorporate randomness generators, fitness evaluation functions, and optimization loops to solve real-world engineering problems.