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'''Simulation-based optimization''' (also known as simply '''simulation optimization''') integrates [[optimization (mathematics)|optimization]] techniques into [[computer simulation|simulation]] modeling and analysis. Because of the complexity of the simulation, the [[objective function]] may become difficult and expensive to evaluate. Usually, the underlying simulation model is stochastic, so that that the objective function must be estimated using statistical estimation techniques (called output analysis in simulation methodology).
Once a system is mathematically modeled, computer-based simulations provide 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. "[https://orbi.uliege.be/bitstream/2268/155988/1/Nguyen%20AT.pdf A review on simulation-based optimization methods applied to building performance analysis]."''Applied Energy'' 113 (2014): 1043–1058.</ref>
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<ref name=Fu>{{cite book|last=Fu|first=Michael, editor|title=Handbook of Simulation Optimization|publisher=Springer|year=2015|url=https://www.springer.com/us/book/9781493913831}}</ref>
<ref>Spall, J.C. (2003). ''Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control''. Hoboken: Wiley.</ref>
=== Statistical ranking and selection methods (R/S) ===
Ranking and selection methods are designed for problems where the alternatives are fixed and known, and simulation is used to estimate the system performance.
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[[Heuristic (computer science)|Heuristic methods]] change accuracy by speed. Their goal is to find a good solution faster than the traditional methods, when they are too slow or fail in solving the problem. Usually they find local optimal instead of the optimal value; however, the values are considered close enough of the final solution. Examples of these kinds of methods include [[tabu search]] and [[genetic algorithms]].<ref name=":0" />
Metamodels enable researchers to obtain reliable approximate model outputs without running expensive and time-consuming computer simulations. Therefore, the process of model optimization can take less computation time and cost.
=== Stochastic approximation ===
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1. Some methods cannot handle optimization problems with more than a few variables; the results are usually not so accurate. However, there are numerous practical cases where derivative-free methods have been successful in non-trivial simulation optimization problems that include randomness manifesting as "noise" in the objective function. See, for example, the following
<ref name=Fu/>
.<ref>Fu, M.C., Hill, S.D. Optimization of discrete event systems via simultaneous perturbation stochastic approximation. ''IIE Transactions'' 29, 233–243 (1997). https://doi.org/10.1023/A:1018523313043
2. When confronted with minimizing non-convex functions, it will show its limitation.
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