Simulation-based optimization: Difference between revisions

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'''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’.<ref>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.</ref>

== Simulation-based optimization methods ==

The simulation-based optimization methods can be categorized into the following groups:<ref>Deng, G. (2007).  ''Simulation-based optimization''  (Doctoral dissertation, UNIVERSITY OF WISCONSIN–MADISON).</ref>

* Response surface methodology (constructing surrogate model, to approximate the underlying function <math display="inline">f</math>)

* Heuristic methods (three most popular methods: [[Geneticgenetic algorithm|genetic algorithms]]s, [[tabu search]], and [[simulated annealing]])

* [[Stochastic approximation]] (category of [[Gradient descent|gradient-based]] approaches.)

* Derivative-free optimization methods

* [[Dynamic programming]] and neuro-dynamic programming

==References==

{{Reflist}}

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