Documentation/Modules

Modules contain all the functionality for optimizing and learning. One Modules may contain an optimization algorithm, an artificial neural network, or a problem to optimize.

Here is a list of documented modules in OpenOpal. Further modules may exist, but may not yet be documented.

Single Objective Optimization Algorithms

Indirect, Deterministic Algorithms

Not implemented, yet.

Direct, Deterministic Algorithms

• OptAlgSIMPLEX: Nelder-Mead Simplex Algorithm

Indirect, Stochastic Algorithms

• Evolutionary Algorithms
  • OptAlgOpO: 1+1 Evolution Strategy with 1/5 Success Rule
  • OptAlgCMA: A Multi-member Evolution Strategy with Covariance Matrix Adaptation

Single and Multi-Objective Optimization Algorithms

Indirect, Stochastic Algorithms

• Evolutionary Algorithms
  • OptAlgMoCMA: Elitist Evolution Strategy with Covariance Matrix Adaptation
• Particle Methods
  • OptAlgMOPSO: Particle Swarm Optimization Algorithm

Design of Experiments

• DoePlanner: A Module Containing Different DoE plans
• RandomSampling: Uniform Random Sampling

Optimization in General

• BoundHandler For Optimization Algorithms Without Bound Handling

Optimization Problems

• ProblemSimple A Simple Test Problem
• ContinuousTestProblems : A Set of Single Objective Test Problem
• ContinuousMOTestProblems : A Set of Multi-Objective Test Problem

Machine Learning

• NeuralNetwork: Artificial Neural Network

Miscellaneous Modules

• SurrogateManager: A Framework for Surrogate Managing