How Many Rays Do I Need for Monte Carlo Optimization? \k;*Ej~.  
While it is important to ensure that a sufficient number of rays are traced to [C.Pzo  
distinguish the merit function value from the noise floor, it is often not necessary to tFO86 !ln  
trace as many rays during optimization as you might to obtain a given level of Sc`W'q^X  
accuracy for analysis purposes. What matters during optimization is that the 1s"6  
changes the optimizer makes to the model affect the merit function in the same way 2y`rS
_2   
that the overall performance is affected. It is possible to define the merit function so /2tgxm$}  
that it has less accuracy and/or coarser mesh resolution than meshes used for T\NvN&h-  
analysis and yet produce improvements during optimization, especially in the early $x)C_WZj?  
stages of a design. s:~3|D][  
A rule of thumb for the first Monte Carlo run on a system is to have an average of at now\-XrS  
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays m?`U;R[  
on the receiver to achieve uniform distribution. It is likely that you will need to L?23Av0W  
define more rays than 800 in a simulation in order to get 800 rays on the receiver. %nSLe~b  
When using simplified meshes as merit functions, you should check the before and YP5V~-O/  
after performance of a design to verify that the changes correlate to the changes of gR
)xw)!  
the merit function during optimization. As a design reaches its final performance 37Q9goMov  
level, you will have to add rays to the simulation to reduce the noise floor so that %lF}!  
sufficient accuracy and mesh resolution are available for the optimizer to find the 4U(W~O  
best solution.