VisualDOC

A general-purpose multidisciplinary design, optimization, and process integration software

VisualDOC

Optimization

Design of Experiments

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logo visualdoc

VisualDOC

a general-purpose multidisciplinary design, optimization, and process integration software.

It is a tool for design process definition, integration, execution, and automation. It can integrate with Excel, Matlab, various CAE software, and user-defined libraries and executables.

visualdoc
visualdoc features

Features:

  • Comprehensive concurrent monitoring and visualization tools
  • Storage and reuse of generated simulation data for post-processing
  • Full debugging support for model execution
  • Ability to interactively inspect and monitor the design process.
  • Supports batch-mode execution
  • Provides programmatic access to the design modules

VisualDOC can perform linear, non-linear, constrained and unconstrained, as well as integer, discrete and mixed optimization. The design modules in VisualDOC are Design of Experiments, Response Surface Approximate Optimization and Optimization.

Optimization

Direct Gradient-based Optimization (DGO)
Direct Gradient-based Optimization (DGO)

VisualDOC calls DOT and BIGDOT to perform gradient-based optimization. The optimization algorithms include Modified Method of Feasible Direction (MMFD), Sequential Linear Programming (SLP), Sequential Quadratic Programming (SQP), Sequential Unconstrained Optimization (BIGDOT), etc.

Non-gradient based optimization (NGO)
Non-gradient based optimization (NGO)

VisualDOC includes state-of-the-art non-gradient based optimization methods. These methods attempt to emulate the natural phenomenon by modeling the optimization process such that it can be mapped to the entities of the natural process in an abstract sense. The methods used by non-gradient-based optimization are Particle swarm optimization (PSO) and Non-dominated Sorting Genetic Algorithm II (NSGAII).

Multi-objective Optimization
Multi-objective Optimization

In VisualDOC, the user can easily generate a Pareto-optimal (PO) front with NSGA-II or any other optimization method. To generate a PO front with single-objective optimization algorithms, scalarization using methods such as weighted-sum, ε-constraint, or compromise programming can be performed, and VisualDOC systematically varies the weight/εvalue/targets to generate the entire PO front.

Design of Experiments

Design of Experiments
Design of Experiments
With Design of Experiments module, user able to:
  • Create an experimental design
  • Construct the response surface model for the design
  • Analyze the characteristics of the design and the approximate model.
  • Generated approximate model optimization
The available DOE design methods are:
  • Factorial Design
  • Central Composite Design
  • Box-Behnken Design
  • Koshal Design
  • Standard Latin Hypercube and Optimal Latin Hypercube Design
  • Taguchi Design
  • Simplex Design
  • Random Design
  • User Defined
  • D-Optimal Design
Response Surface Approximate Optimization

The RSA component combines Optimization (OPT), Design of Experiments (DOE), and Response Surface Modeling (RSM) to improve optimization efficiency. The approximation is used as a surrogate for the underlying computationally expensive analyses and is incrementally refined as the optimization proceeds. The user can choose the DOE technique, the optimization algorithm, and the approximation model to use with the RSA component.

The available approximation models are:
  • Linear
  • Mixed: Linear + Interaction
  • Mixed: Linear + Quadratic
  • Full Quadratic
  • Forward Stepwise Regression

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