Simulation-based optimization
Simulation based optimization integrates optimization techniques into simulation analysis. Because of the complexity of the simulation, the objective function may become difficult and expensive to evaluate.
To understand the characteristic of a system, computers help a lot these days. Once the real system is mathematically modeled, computer based simulations provide the information about its behavior. Parametric simulation methods can be used to improve the performance of a system. In this method, the input of each variable is varied with other parameters remaining constant and the effect on the design objective is observed. This is a time consuming method and improves the performance partially. To obtain optimal solution with minimum computation and time, the problem is solved iteratively where in each iteration the solution moves closer to the optimum solution. Such methods are known as ‘numerical optimization’ or ‘simulation-based optimization’.
Simulation-Based Optimization Methods
- Response surface methodology (constructing surrogate model, to approximate the underlying function f)
- Heuristic methods (three most popular methods: genetic algorithms, tabu search and simulated annealing)
- Stochastic approximation (category of gradient-based approaches.)
- Derivative-free optimization methods
- Dynamic programming and neuro-dynamic programming