Optimization Algorithms

Built in optimization algorithms:

  • Particle Swarm optimizes the project by simulating the movement of a bird flock or a swarm of bees. It is very useful for optimization problems involving roughly 4-10 variables.
  • Genetic uses evolutionary logic for finding the best solution. It is one of the best optimization algorithms for multivariable and multicriterion optimization today.
  • Simulated Annealing uses logic of micro systems cooling for finding the best solution. This is one of a few algorithms that can be used both for coarse optimization and fine tuning.
  • Random assumes that every optimization parameter is a uniformly distributed random variable in a given space and tries to find the solution by random guessing. Although a slow algorithm by default it is one of the best choices for the first step in a hybrid optimizations.
  • Gradient is based on an estimation of the gradient of the error function, which is calculated using given optimization criteria, and searching for the minimum in the direction in which function decreases fastest. This algorithm converge to the minimum practically quicker than any other algorithm.
  • Simple Search systematically explores the optimization space.
  • Simplex is based on Nelder-Mead simplex algorithm that proved itself for many times in practice because of its ability to skip from one minimum to a better one. It is very quick and most robust local optimizer. Therefore it is ideal for the second step in hybrid two-step optimizations.

Unique feature is that two optimization procedures with different algorithms can be performed in succession. The first one is used for coarse optimization and the second one is used for fine tuning. Thus obtained hybrid methods combine favorable traits of different algorithms, resulting in overall increase of optimization efficiency.

Using hybrid methods, WIPL-D Optimizer enables you to find not only the global minimum, but also a set of the best local minima. Insight into other local minima enables you to find the solution which meets the criteria not included into the cost function (e.g. the solution that could be optimal for fabrication).