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MOP

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Multi-Objective Problems (MOP)


\begin{align}
\vec f(\vec x) \to \vec F \\
\text{subjected to} \\
\vec g(\vec x) & \le \vec 0 \\
\vec h(\vec x) & = \vec 0  \\
\vec x_l \le & \vec x  \le \vec x_u \\
\vec x \in & R^n \\
\vec f: R^n \to R^m\\
\vec F \in (R \cup \{-\infty,\infty\})^m\\
\end{align}


Solver License Made by Info
(since v 0.38)
interalg
BSD Dmitrey Can handle global constrained MOPs with specifiable accuracies, continuous, boolean, discrete and (since v. 0.39) categorical variables, (since v. 0.39) general logical constraints. You can be 100% sure your result covers whole Pareto front according to the required tolerances on objective functions. If you want to speedup interalg on a MOP, first of all pay attention to its parameter maxActiveNodes and interalg MOP parameter sigma, and nProc (default: 1) for multi-CPU systems. Trajectories for different nProc should be same, but sometimes differ due to roundoff errors.

future plans for interalg include some speedup and better multiprocessing, possibility to start from a Pareto front obtained by another (inexact) MOP solver

See also:

Retrieved from "http://openopt.org/MOP"
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