Gradient-based optimization algorithms are very efficient and stable for finding local optima in well-behaved and smooth design spaces. These can provide significant insight into performance tradeoffs that depend on continuous design parameters.

Genetic algorithms are extremely powerful for finding optimum solutions in a design space full of local minima. Our team has an in-house tool and access to open-source evolutionary solvers, with the ability to tune these algorithms to find meaningful solutions.