Author: Ron Averill

In poker, a player declares “all in” when he decides to bet all of his remaining chips on the cards in his hand. He then waits nervously while the remaining cards are dealt, knowing that he will soon either win big or lose all of his chips (“go bust”).

A similar gamble occurs when you apply some optimization approaches based on Design of Experiments (DOE) concepts.  In this case, the actual objective function being minimized is evaluated at a predetermined set of design points. Then, a simple approximation of the objective function is developed by fitting an analytical function to these points. This approximate function is often called a response surface (also a surrogate function). The optimization search is then performed on the response surface, because evaluations of this simpler function are usually much quicker than evaluations of the actual objective function.

However, by defining all of your design evaluations ahead of time (going “all in”), you are risking that the corresponding response surface may not accurately represent the true objective function. If the surface fit is not accurate enough, then searching the response surface may not really give you the optimal design. In fact, it is common for an inaccurate response surface to completely mislead the optimization search, resulting in a very poor solution. So, while an accurate response surface could yield an optimized solution at lower cost than some other optimization approaches, a poorly fit surface may yield no useful results at all (you’ll “go bust”).  Continue reading →

Multi means “many” or “multiple.” Multidisciplinary design optimization (MDO) has become popular largely because it allows engineers to optimize over many different disciplines at the same time.

For example, you can use MDO to simultaneously optimize a vehicle body for structural, aerodynamic, thermal and acoustic behaviors. In addition, you can directly include non-performance measures, such as cost and manufacturability, in the optimization statement.  Continue reading →

Engineers and scientists like to know how things work. They seem to be born with an inner drive to understand the fundamental nature of things. So, naturally, they may have some reservations about using an algorithm if the way it functions is not clear.

When we can’t see the details about how something works, we often refer to it as a black box. Input goes in and output comes out, without any knowledge of its internal workings.

Black box sometimes has a negative connotation, because knowing how something works is usually a good thing. But if we evaluate the idea of a black box, we find that many common processes and tools – including the human brain – actually fall into this category.

For example, most users of the finite element method have some basic knowledge of its underlying mathematical theory. But many of the element types available in commercial software packages are based on advanced formulations that few users completely understand. These advanced formulations are necessary to overcome deficiencies in the element behavior, and users can apply them accurately without knowing all the mathematical formalities. There are many similar examples in computational mechanics.  Continue reading →

The human brain is capable of making quick and effortless judgments about people, objects or ideas that it has not previously encountered. This sort of unreasoned insight is often called intuition. In his article, “The Powers and Perils of Intuition” (Scientific American MIND, June 2007, pp 24–31), David Myers describes two types of influence that shape our intuition.

The first is the development of mental shortcuts, or heuristics, which allow us to make snap judgments, often correctly. For example, our intuition tells us that blurry objects are farther away than clear ones. This is often a helpful assumption, except that on foggy mornings, a car in front of you may be much closer than intuition tells you it is.

The second influence on intuition is “learned associations” or life experiences that guide our actions. This explains why we may be suspicious of a stranger who resembles someone who once threatened us, even if we do not consciously make the association. Similarly, an experienced engineer can often quickly solve a problem that resembles one he worked on many years ago, even if the details of that project are mostly forgotten.   Continue reading →

I have a great idea for a new reality adventure television series.

The basic premise is simple. Contestants are blindfolded and driven to a starting location on the side of a mountain. When the race begins, each contestant must find a path to the base of the mountain as quickly as possible. The blindfolds make it impossible for contestants to detect the contours and obstacles in the landscape.

When contestants are working alone, the strategies they can use are limited. If the terrain is smooth, like a rolling pasture, then contestants might find a successful path by taking small steps in several different directions, and then choosing the direction that leads downward. When the contestant feels that path starting to flatten out or trend upward, she knows it’s time to stop and choose a new downward direction. Repeating this process many times should lead each contestant to the bottom of the nearest valley, which depends on the starting location. The first one to the bottom wins!  Continue reading →

Yes, this is an odd title for a blog post that is meant to promote optimization. But this opinion is expressed more often than you might think, especially among engineers who have tried to apply classical optimization technology to their challenging design problems.

So, what is causing smart people to form this opinion? I believe there are four types of experiences that cause people to lose faith in optimization:

1. The optimized solution was not as good as expected.

Continue reading →

Conducting more design iterations can lead to higher-quality designs and increased innovation. So, when faced with a tight design schedule, the goal of many organizations is to iterate faster. But in most cases, performing faster manual design iterations doesn’t make the design process more productive.

Consider the consequences of maximizing iteration throughput for a typical manual design process. Let’s assume a simple, but familiar, scenario in which each iteration involves the following steps:

1. Create a CAD model of the geometry,
2. Build a math model to predict performance,
3. Execute the math model, and
4. Interpret its results.

Continue reading →

Made popular by the Seinfeld television series, the Soup Man restaurant in New York City demands that customers know what kind of soup they want before arriving at the counter. Signs are prominently displayed, stating the rules in several languages:

FOR THE MOST EFFICIENT AND FASTEST SERVICE

Failure to follow these rules may result in the harshest of penalties — no soup for you!

Like the Soup Man restaurant, the optimization process is not well suited to those who don’t have a clear set of goals in mind.  Continue reading →

The word parsimonious means thrifty, economical, frugal, and sometimes even stingy. It is an unusual word to use when describing optimization, but it is meaningful here in two ways.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

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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.”

A similar situation occurs in product development when selecting between two competing design concepts. A more optimized version of concept A may appear better than a version of concept B that has not been optimized. But concept B may have a lot more potential for improvement, a bigger upside.

The performance of a single example of a concept is not usually a good measure of the concept itself.

How optimized is concept A? What level of performance could be attained by concept B? We seldom know the answers to these questions prior to performing an optimization study.   Continue reading →