Simulation Based Optimization

Simulation-based optimization (SBO) tackles complex problems where analytical solutions are unavailable by iteratively refining model parameters to optimize a desired outcome through repeated simulations. Current research emphasizes efficient exploration of the parameter space using diverse algorithms, including genetic algorithms, Bayesian optimization, and gradient descent methods, often enhanced by surrogate models like neural networks to reduce computational cost. SBO finds applications across numerous fields, from optimizing production systems and machine learning hyperparameters to improving the design of complex systems like hypersonic vehicles and evacuation plans, enabling better decision-making in scenarios with high dimensionality and stochasticity.