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.
Once a 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 the 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’.[1]
Simulation-based optimization methods
Simulation-based optimization methods can be categorized into the following groups:[2][3]
- Response surface methodology (constructing surrogate model, to approximate the underlying function )
- 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
References
- ^ Nguyen, Anh-Tuan, Sigrid Reiter, and Philippe Rigo. "A review on simulation-based optimization methods applied to building performance analysis."Applied Energy 113 (2014): 1043–1058.
- ^ Fu, Michael, editor (2015). Handbook of Simulation Optimization. Springer.
{{cite book}}:|first=has generic name (help)CS1 maint: multiple names: authors list (link) - ^ Deng, G. (2007). Simulation-based optimization (Doctoral dissertation, UNIVERSITY OF WISCONSIN–MADISON).