Min/Max Optimisation

Modified on Fri, 12 May 2023 at 08:31 AM


Min/Max Optimisation finds the inputs that maximise or minimise one selected variable.


The drive of optimisation for an engineer is to use resources efficiently and effectively to provide a product with the best possible performance (e.g. minimising cost, maximising strength). Min/Max Optimisation can help engineers to find the inputs of a system that will generate the optimal output, and the maximum/minimum value possible for an output.

How to use

When using Min/Max Optimisation, a model must already be trained beforehand.

  • Select Model: Select a trained model in the field Model.
  • Output to optimise: Select the output of the model in which the algorithm will find the maximum/minimum value.
  • Find: Select whether the variable in Output to optimise will be maximised or minimised.
  • Restrict inputs: If you want to restrict the input range in which the optimisation will be applied, the Restrict inputs box should be ticked. Sliders for all inputs will then appear and you can define the optimisation range for each input with these. 
    • If the input range is not changed it is not restricted. i.e., the input range is only restricted if the sliders are changed from their initial range.
    • An input can be restricted to a fixed value instead of a range by ticking the box Fixed.
    • When optimising the output, this option can be useful if certain inputs are more expensive, harder to change or even cannot be modified. Reminder: by limiting/fixing the inputs, the optimisation towards the target value will probably be less effective.
  • Optimisation Method: Two options are available.
    Iterative Search (Slow)This method uses differential evolution (stochastic) algorithm to find the best model inputs for optimising the fitness function. The iterative search involves starting with an initial solution and then systematically improving it by making small adjustments until the optimal solution is found. This process is repeated until the desired level of accuracy is achieved.
    The user can define:
    • Number of recommended designs, which will be the best designs found.
    • Limit number of iterations, which is the maximal number of repetitions the optimisation algorithm will run until it stops.
    Global Sampling (Fast)

    This method randomly samples inputs on the design region and calculates the output values. The output closer to the target value (based on the fitness function) is ranked higher on the recommended designs.

    The user can define:

    • Number of recommended designs, which will be the best designs among all explored designs.
    • Number of points to sample, which is the total number of random designs that will be generated and evaluated.

    The Global Sampling method is faster than the Iterative Search, however for models with a high number of inputs, the sampling can become sparse and an optimum value becomes less likely to be found.

  • Optimal Data Name: Choose a name for the list of the optimal design data set.

Step Results

  • Optimal Design: The step outputs a one-row dataset showing the input combinations and calculated output.

More on this step

The Min/Max Optimisation only allows minimising/maximising a single target (output). If you need to do an optimisation with multiple targets you should use Targeted Optimisation instead.

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