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 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.
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.
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).
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.
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.
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