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 OpenDino. Further modules may exist, but may not yet be documented.

Single Objective Optimization Algorithms

Indirect, Deterministic Algorithms

Indirect algorithms use gradient or higher order derivative information in the optimization.

Not implemented, yet.

Direct, Deterministic Algorithms

These algorithms neither use gradient information nor stochastic processes.

• OptAlgSIMPLEX: Nelder-Mead Simplex Algorithm

Indirect, Stochastic Algorithms

These algorithms do not use gradient information but require stochastic processes (i.e. random numbers) in their search.

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

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 (Monte Carlo)

Optimization in General

• BoundHandler: For Optimization Algorithms without Bound Handling
• ConstraintHandler : For Optimization Algorithms without Constraint 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