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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>{{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>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 </ref>.
2. When confronted with minimizing non-convex functions, it will show its limitation.
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