Regions of preference and avoidance represent a new approach to optimization and design improvement. It is a statistical approach to optimization, which defines a region for improvement and a region for avoidance. The region of preference is a new distribution for the input variable, which one wants to achieve in order to have stable statistical improvement. In other words, truncating the input distribution in a proper way, one can achieve stable improvement. Contrary, the region of avoidance represents a region, which leads to low results in the final distribution. In other words, a region in the input distribution, which needs to be avoided in order to have a good design. However, these regions can overlap. The overlap is closely connected to the influence/sensitivity of the given variable. For very influential variables, these regions will not overlap and one should avoid the region of avoidance as it will almost surely lead to bad final design. On the other hand, for non-influential variables, these regions will overlap and re-design the input distribution according to the region of preference will lead to small improvement. Due to the statistical nature of the optimization, setting only a few variables to the region of preference will still lead to an improvement in the final design. However, the improvement will not be as large as predicted by our code. Nevertheless, this allows for improving the final design according to criteria, which are not (or cannot) be specified.
We define the region of preference to be on the right side of the final distribution (maximization process) and the region of avoidance on the left side of the final distribution. If the minimization is needed, then the process is reversed, i.e. the regions of avoidance become the regions of preference. Nevertheless, all the conclusions stay the same.In the section Region of preference/avoidance -> Optim are showed the global maximum and minimum values of the output function, and the combination of inputs that define it. The domain of the inputs can be either the original distribution, the preference/avoidance distribution, or a combination of these two.
Fig. 1: Regions of preference and avoidance – Optim
First, it is necessary to load file XDis.txt, which contains data for the original input distributions. Once the file is loaded, it is needed to select the variables which domain can be changed to the preference/avoidance distributions, showed in Region of preference/avoidance -> Inputs. After pressing the Apply button, the combination of variables that give the maximum and the minimum output are displayed.
To store selected results in File (upper left corner) select Save. It will allow you to browse in folders starting in the project folder. The results will be saved in an Excel format (.xls).
[ Placeholder content for popup link ]
WordPress Download Manager - Best Download Management Plugin