There are many factors that influence the performance of OptQuest. For example, consider two optimization methods, A and B, applied to an investment problem with the objective of maximizing expected returns. When you evaluate the performance of each method, you must look at which method:
Below is the performance graph for the two hypothetical methods.
Figure 70, Performance comparison shows that although both methods find solutions with a similar expected profit after 10 minutes of searching, method A jumps to the range of high-quality solutions faster than B. For the criteria listed previously, method A performs better than method B.
While using OptQuest, you will obtain performance profiles similar to method A. OptQuest’s search methodology (see the references in Appendix B) is very aggressive and attempts to find high-quality solutions immediately, causing large improvements (with respect to the initial solution) early in the search. This is critical when OptQuest can perform only a limited number of simulations within the available time limit.
However, several factors affect OptQuest’s performance, and the importance of these factors varies from one situation to another. The following is a list of the relevant factors that directly affect the search for an optimal solution: