CONCLUSION and future work

This work proposed the use of WMS for future optimization by similarity. The method intends to find the experimental region where a model with desirable characteristics is a good descriptor of the data at hand. An evaluation case using function AOG_1 was presented. According to these results, the method demonstrates the potential to find regions of similarity between two responses where optimality can be a pattern of interest.
The evaluations of the method in seven unconstrained global optimization test functions served to show the use of window of maximum similarity in examples of functions with different shapes. Also, it was observed in the evaluations that the WMS method potentially detected zones of maximum similarity between the different test functions and a quadratic function.
According to these results, the method demonstrates the potential to find regions of similarity between two responses where optimality can be a pattern of interest and can be a useful tool for exploration of simulated data to find, at least a local optimum.
In many cases, the WMS obtained by the method were limited to take the minimum size or epsilon value assigned, which is why future work includes:
  1. Substitute single composite objective by multiple criterion optimization, as presented in [5].
  2. Include more variables to evaluate test functions on.
  3. Experiment using alternative metamodels with different forms.
  4. Use design of experiment to sample from the experimental region.