|Basic Principles of a New Optimization Paradigm|
Optimization Viewpoints Blog
by Ron Averill
Change is often viewed as the result of a scientific discovery or the development of a breakthrough technology. But there's usually a lot more to the story. To quote Paul Saffo - technology doesn't drive change, it enables change.
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.
Too often, the real value of a new technology becomes enslaved by old notions about how things work or what is possible. To realize the full advantages of a new technology, we need to look at things differently and accept new possibilities.
Recently, a new paradigm has emerged for design optimization - one that is enabled by game-changing discoveries in optimization search technology and that leverages ongoing advances in computing power and virtual prototyping. This paradigm is free of the constraints imposed by previous technology and is based on a set of principles that allow a more natural flow of thought and effort:
- Start with a good concept, not necessarily a good design. Let the optimizer do the work of searching for good designs.
- Optimize early and often. Not just at the end of the design cycle or after all other means have been exhausted.
- Define the design problem you need to solve. Not the one that can be solved by a certain optimization strategy.
- Optimize the system interactions. Not just the components.
- Let the optimization algorithm figure out how to search the design space. There's often no way to guess ahead of time which search method and tuning parameters will work best.
- Don't perform optimization using models of your models. Response surface or surrogate models often increase effort and error.
- Be an engaged participant in the optimization search. Leverage your knowledge and intuition during a collaborative search process.
- Care about the sensitivities of your final design. Not those of your initial guess, which often have no bearing on the final design.
A design optimization process based on these principles is simpler and consistently yields better solutions in less time.
Implementing this process requires solid engineering skills, but you don't need to be an expert in optimization theory.
Design optimization software built around these principles is also simpler to use, because the workflow for defining, solving and post-processing an optimization study is cleaner, with fewer steps and tricky decisions to make.
Over the next several weeks, I will provide more detail about each of the above principles. In the meantime, be careful with that chainsaw!
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