Optimizers search for goals set by users. These might be lower cost, weight reduction, manufacturability, lower stresses, or better flow characteristics. It's now possible to combine automated modeling, simulation, and design-optimization to explore a potentially enormous number of possibilities while reducing design times. Instead of performing manual design iterations based on intuition and experience, Heeds automates the trial-and-error process by exploring more options more thoroughly and more quickly than manually possible to find a best one. The software can do in days what would take a person weeks.

This automated process uses algorithms uncover creative not obvious to even experienced engineers. This is particularly effective with shape problems, which can involve potentially hundreds of design variables.

A hybrid adaptive search algorithm in Heeds called Sherpa explores thousands of designs to find a best solution. While other optimizers use a single search algorithm, Sherpa combines the best attributes of many algorithms. The software simultaneously performs global and local optimization, exploring the design for new concepts while fine-tuning the promising designs toward their optimal form. The adaptive nature of Sherpa makes it self-tuning. The algorithm “learns” about the problem as it searches, and configures itself to be more effective. Because there are no tuning parameters, even non-experts can use Sherpa to optimize designs.