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Design Modules in VisualDOC

VisualDOC can perform linear, non-linear, constrained and unconstrained, as well as integer, discrete and mixed optimization. The design modules in VisualDOC include: Gradient-based, Non-gradient-based, and Response Surface Optimization along with Design of Experiments and Probabilistic Analysis and Optimization. Multi-objective optimization is also available in VisualDOC. The details of each design module are as follows:


Direct Gradient-based Optimization (DGO)
VisualDOC calls DOT and BIGDOT to perform gradient-based optimization. The following optimization algorithms are included.

  • Modified Method of Feasible Direction (MMFD)
  • Sequential Linear Programming (SLP)
  • Sequential Quadratic Programming (SQP)
  • Sequential Unconstrained Optimization (BIGDOT)
  • Broydon Fletcher Goldfarb Shanno (BFGS)
  • Fletcher-Reeves (FR)
Optimization Property Editor

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 following non-gradient-based optimization methods are included.

  • Particle swarm optimization (PSO)
  • Non-dominated Sorting Genetic Algorithm II (NSGAII)

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/ε-value/targets to generate the entire PO front.