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What’s New in the Maple Global Optimization Toolbox

Now powered by Optimus®!

Maplesoft has partnered with Noesis Solutions to develop a new version of the Maple Global Optimization Toolbox that is powered by Optimus technology. Optimus, from Noesis Solutions, is a platform for simulation process integration and design optimization. Its powerful, proven optimization technology is now available to Maple users as the engine behind the Global Optimization Toolbox, reducing execution times and expanding the scope of problems this toolbox can solve.

The updated toolbox is fully compatible with existing code, so you do not need to make any changes to take advantage of the new solvers.

The new toolbox features new solver methods and many more options that allow you to guide the search for the solution based on your knowledge of the problem. Using these new methods and options, you can reach your solutions faster and solve more problems than ever before.

Here are the new solver methods, together with a selection of new options:

Differential Evolution Algorithm

  • Average step width - the variation in design variables used as stopping criterion
  • Population size - the number of designs evaluated during each iteration
  • Inverse crossover probability - the probability that variables might be the same as predecessors without adjustment
  • Target weighting factor - applies weighted differences of the variables of the randomly selected designs of the previous generation, allowing you to set the balance between computation speed and probability of success

Adaptive Stochastic Search Methods

  • Theta method - methods for maximizing the predictive quality of the model, maximizing the likelihood of the set of points with respect to the model, and minimizing the semi-norm of the correlation matrix
  • Number of sigma - how much attention is given to the estimated standard deviation when selecting the next points
  • Nugget - the smoothing factor for the kriging response surface model
  • Optimum search method - different methods for locating new promising points in the design space

 

 

 

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