# MDO formulation options¶

List of available MDO formulation: BiLevel - DisciplinaryOpt - IDF - MDF -

## BiLevel¶

### Description¶

A bi-level formulation.

This formulation draws an optimization architecture that involves multiple optimization problems to be solved to obtain the solution of the MDO problem.

Here, at each iteration on the global design variables, the bi-level MDO formulation implementation performs:

1. a first MDA to compute the coupling variables,

2. several disciplinary optimizations on the local design variables in parallel,

3. a second MDA to update the coupling variables.

### Options¶

• apply_cstr_to_system, boolean - If True,

• apply_cstr_tosub_scenarios, boolean - If True,

• maximize_objective, boolean - If True, the objective function is maximized.

• mda_name, string - The name of the MDA class to be used.

• multithread_scenarios, boolean - If True and parallel_scenarios=True, the sub-scenarios are run in parallel using multi-threading; if False and parallel_scenarios=True, multi-processing is used.

• parallel_scenarios, boolean - If True, the sub-scenarios are run in parallel.

• reset_x0_before_opt, boolean - If True, restart the sub optimizations from the initial guesses, otherwise warm start them.

## DisciplinaryOpt¶

### Description¶

The disciplinary optimization.

This formulation draws the architecture of a mono-disciplinary optimization process from an ordered list of disciplines, an objective function and a design space.

### Options¶

• maximize_objective, boolean - If True, the objective function is maximized.

## IDF¶

### Description¶

The Individual Discipline Feasible (IDF) formulation.

This formulation draws an optimization architecture where the coupling variables of strongly coupled disciplines is made consistent by adding equality constraints on the coupling variables at top level, the optimization problem with respect to the local, global design variables and coupling variables is made at the top level.

The disciplinary analysis is made at a each optimization iteration while the multidisciplinary analysis is made at the optimum.

### Options¶

• maximize_objective, boolean - If True, the objective function is maximized.

• normalize_constraints, boolean - If True, the outputs of the coupling consistency constraints are scaled.

• parallel_exec, boolean - If True, all constraints and objectives are computed in parallel. At every iteration, all disciplines are executed in parallel. Otherwise, a separate constraint is created for each discipline with couplings.

• start_at_equilibrium, boolean - If True, an MDA is used to initialize the coupling variables.

• use_threading, boolean - If True and parallel_exec=True, the disciplines are run in parallel using multi-threading. If False and parallel_exec=True, multi-processing is used.

## MDF¶

### Description¶

The Multidisciplinary Design Feasible (MDF) formulation.

This formulation draws an optimization architecture where:

• the coupling of strongly coupled disciplines is made consistent by means of a Multidisciplinary Design Analysis (MDA),

• the optimization problem with respect to the local and global design variables is made at the top level.

Note that the multidisciplinary analysis is made at a each optimization iteration.

### Options¶

• main_mda_class, string - The name of the class used for the main MDA, typically

• maximize_objective, boolean - If True, the objective function is maximized.

• sub_mda_class, string - The name of the class used for the sub-MDA.