First, the purpose of optimization is to minimize a function. The intent is not a meager reduction, but absolute minimization. This seems pretty stingy, but in a useful way.
Second, and of greater interest here, the process of optimization must be efficient, economical, and thrifty. That is, finding an optimized solution to a problem should take as little of your time and resources as possible. Unfortunately, in many real-world applications, time is the largest barrier to realizing the true value of automated optimization.
In theory, you should be able to find an optimized solution whenever you have a good system analysis model and an appropriate search algorithm. But if the model requires hours or even days of CPU time for each design evaluation, and the algorithm requires a large number of evaluations, then the total time required to reach that optimized solution may turn out to be completely impractical.
Let’s consider the cost factors involved. The total CPU time needed to find an optimized solution is defined this way:
where NSOL is the number of optimization solutions performed, and the expression inside the brackets represents the total search time per iteration of the optimization solution.
Based on this formula, there are only four ways you can reduce the solution time for an optimization study. You can
High school tryouts. College recruiting. Pro draft day. At every level of athletics, coaches face a tough pre-season decision. Select player A, a better than average athlete who has worked diligently for many years to maximize his potential under the tutelage of top coaches. Or choose player B, whose present skill level is not quite as high but who has greater raw athletic ability, is coachable and has real potential to be a superstar – a trait that coaches call “upside.”
“Over a dozen equation solvers are available to approximate the solution of your problem, and each solver contains a rich set of parameters that you can define to tune the solver’s performance. To maximize the accuracy of your solution and the efficiency of the solution process, simply choose the solver that is intended for your problem type, and then tune it properly. Though it is often not possible to classify your problem type beforehand, usually the right solver can be identified within 3-5 attempts. Then, you can use an iterative tuning process to make the solution even more accurate and efficient.” 
Thomas Edison demonstrated the first long-lasting, high-quality light bulb in 1879. His successful design resulted from a long and laborious trial-and-error search for the best filament material, a process we now call the Edisonian approach.