optimization.multiobjective

optimization.multiobjective.paretoFront(P_matrix)

Function paretoFront()

Parameters:

P_matrix – P_matrix

Returns:

P_matrix

Returns:

idxs

Filters a set of points P according to Pareto dominance, i.e., points that are dominated (both weakly and strongly) are filtered.

Inputs:

  • P_matrixN-by-D matrix, where N is the number of points and D is the

    number of elements (objectives) of each point.

Outputs:

  • P_matrix : Pareto-filtered P_matrix

  • idxs : indices of the non-dominated solutions

Example:

p = [1 1 1; 2 0 1; 2 -1 1; 1, 1, 0];
[f, idxs] = paretoFront(p)
f = [1 1 1; 2 0 1]
idxs = [1; 2]
optimization.multiobjective.pareto_curve_heuristics(r1, r2)

Function pareto_curve_heuristics()

Parameters:

  • r1 – r1

  • r2 – r2

Returns:

Figure handles

optimization.multiobjective.damping_vs_reactive(p, b)

Function damping_vs_reactive()

Parameters:

  • p – Parameter struct

  • b – Design variable bounds struct

Returns:

r1

Returns:

r2

Function pareto_search()

Parameters:

  • p – Parameter struct

  • b – Design variable bounds struct

  • filename_uuid – filename_uuid

Returns:

Design variable vector

Returns:

fval

Returns:

residuals

Returns:

tol

Returns:

Parameter struct