Optimization techniques in RSM are used to find the optimal levels of the factors that produce the best response.
Several methods are employed in RSM for optimization:
1. Gradient Descent:
This iterative method moves from an initial point towards the steepest descent direction to find the minimum of the function.
2. Desirability Function:
Combines multiple responses into a single composite response.
Each response is transformed into a desirability value ranging from 0 (least desirable) to 1 (most desirable).
The overall desirability is then optimized.
3. Numerical Methods:
These include techniques such as the Nelder-Mead method, genetic algorithms, and others, which are computational algorithms to find the optimal conditions.
Steps in Optimization:
1. Define the objective function:
This could be to maximize, minimize, or achieve a target value of the response.
2. Set constraints
Constraints may be set on the factor levels or the responses.
3. Apply optimization algorithms:
Use appropriate optimization techniques to find the best settings for the factors.