pymoo - Multi-Objective Optimization with pymoo
Solves single- and multi-objective optimization problems with evolutionary algorithms, constraint handling, benchmark problems, visualization, and Pareto-based decision making.
Tags
Updated: 2026-10-07Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Solve single-objective problems
- Solve multi-objective problems
- Run evolutionary algorithms
- Handle optimization constraints
- Benchmark optimization algorithms
- Customize genetic operators
- Visualize optimization results
- Select Pareto solutions
Inputs
- Optimization problem definition
- Objective functions
- Constraint functions
- Decision variable bounds
- Algorithm configuration
- Termination criteria
- Reference directions
- Decision preferences
Outputs
- Optimized decision variables
- Objective values
- Constraint violations
- Pareto fronts
- Algorithm history
- Optimization plots
- Selected solutions
Requirements
- Python 3.10 or later
- pymoo package
- NumPy
- SciPy
- matplotlib for visualization
- autograd for gradient features
- joblib for JoblibParallelization