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MOP

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Multi-Objective Problems (MOP)

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

FuncDesigner MOP examples:
- simplest (variables: 1, goals:2, unconstrained)
- advanced (variables: 2, goals:3, constrained)
- expert (variables: 7 continuous + 2 discrete, goals: 2, constrained, real-time or final graphical output, parallel calculations, export result to xls file), see also its graphic output and result automatically exported to xls file

Solver	License	Made by	Info
(since v 0.38) interalg	BSD	Dmitrey	Can handle global constrained MOPs with specifiable accuracies and both discrete and continuous variables. 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

MOP

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Free Python-written numerical optimization software suite. Visitors since 2007/10/24: