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
.
Functions
fgoalattain | Solve multiobjective goal attainment problems |
fminimax | Solve minimax constraint problem |
Live Editor Tasks
Optimize | Optimize or solve equations in the Live Editor (Since R2020b) |
Topics
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 dedicatedfminimax
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.