Main Content

Multiobjective Optimization

Solve multiobjective optimization problems in serial or parallel

Solve problems that have multiple objectives by the goal attainment method. For this method, you choose a goal for each objective, and the solver attempts to find a point that satisfies all goals simultaneously, or has relatively equal dissatisfaction. One important special case of this problem is to minimize the maximum objective, and this problem has a special solver, fminimax.


fgoalattainSolve multiobjective goal attainment problems
fminimaxSolve minimax constraint problem

Live Editor Tasks

OptimizeOptimize or solve equations in the Live Editor


Multiobjective Solutions

Generate and Plot Pareto Front

Example showing how to plot a Pareto front in a two-objective problem.

Compare fminimax and fminunc

Shows how minimax problems are solved better by the dedicated fminimax function than by solvers for smooth problems.

Multi-Objective Goal Attainment Optimization

This example shows how to solve a pole-placement problem using multiobjective goal attainment.

Using fminimax with a Simulink® Model

Example showing how to minimize the maximum discrepancy in a simulation.

Signal Processing Using fgoalattain

Example showing filter design using multiobjective goal attainment.

Minimax Optimization

This example shows how to solve a nonlinear filter design problem.

Parallel Computing

What Is Parallel Computing in Optimization Toolbox?

Use multiple processors for optimization.

Using Parallel Computing in Optimization Toolbox

Perform gradient estimation in parallel.

Improving Performance with Parallel Computing

Investigate factors for speeding optimizations.

Algorithms and Other Theory

Multiobjective Optimization Algorithms

Minimizing multiple objective functions in n dimensions.

Smooth Formulations of Nonsmooth Functions

Reformulate some nonsmooth functions as smooth functions by using auxiliary variables.

Optimization Options Reference

Explore optimization options.