Many engineers still resist the use of optimization algorithms to help improve their designs. Perhaps they feel that their hard-earned intuition is just too important to the solution process. In many cases, they are right.
At the same time, most optimization algorithms still refuse to accept input from engineers to help guide their mathematical search. The assumption is that the human brain cannot possibly decipher complex relationships among multiple system responses that depend upon large numbers of connected variables. Unfortunately, this is true. Continue reading
I am currently training to compete in my first sprint triathlon race. Well, compete may be an exaggeration, and there won’t be much sprinting. But I do hope to cross the finish line before the sun sets.
“Creativity is just connecting things. When you ask creative people how they did something, they feel a little guilty because they didn’t really do it, they just saw something. It seemed obvious to them after a while. That’s because they were able to connect experiences they’ve had and synthesize new things. And the reason they were able to do that was that they’ve had more experiences or they have thought more about their experiences than other people.”“Unfortunately, that’s too rare a commodity. A lot of people in our industry haven’t had very diverse experiences. So they don’t have enough dots to connect, and they end up with very linear solutions without a broad perspective on the problem. The broader one’s understanding of the human experience, the better design we will have.”
In multi-disciplinary design optimization (MDO), a similar question comes up: “how many design evaluations are needed to converge on the optimal solution?” Unfortunately, as with money, there is no universal answer. The number of evaluations required depends on the problem you are trying to solve, your approach to solving it, and the starting point for the solution. Let’s unpack this a bit. 
What are the three most important things to consider when buying a house? Location. Location. Location. The repetition intentionally over-emphasizes this point: if the location of your new real estate purchase is not good, then the details of the home don’t really matter.
You may have heard the story about the woman who always sliced about one inch off the end of a large roast before placing it in the pan to be cooked. When asked why she did this, she did not know the reason. But she was sure that it was important, because her mother always did exactly the same thing.
When Thomas Edison developed the first long-lasting, high-quality light bulb in 1879, his successful design was the result of a lengthy and laborious trial-and-error search for the best filament material, a process we now call the Edisonianapproach.
Despite these impressive exhibitions, machines cannot think like humans – yet. For example, given different sets of information, humans have a natural ability to detect patterns and perceive order. Computers, on the other hand, must be told what types of pattern structures to look for.
When the chainsaw was first introduced, I wonder how many lumberjacks tried dragging it back and forth against a tree, expecting it to work the same way as a hand saw. Figuring out the best use of a new technology is just as important, and sometimes as difficult, as developing the technology in the first place.
When martial artist Bruce Lee offered the above advice about being adaptable, I doubt that he was referring to mathematical optimization. But these words of wisdom are certainly relevant to optimization algorithms.