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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.

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

Design of Experiments

Optimization in General

Optimization Problems

  • ProblemTruss : The goal is to opzimize the thickness of 10 trusses. The weight of the truess, maximum stress, and displacement can each be set either as objective or constraint.

Machine Learning

Miscellaneous Modules