pymoo - Multi-Objective Optimization in Python
Solve single- and multi-objective optimization problems with evolutionary algorithms, constraints, benchmarks, Pareto fronts, and decision-making methods.
Tags
Updated: 2026-10-02optimizationmulti-objectiveevolutionary algorithmsPareto frontNSGA-IINSGA-IIIconstraint handlingPython
Capabilities
What this skill does
- Solve optimization problems
- Find Pareto-optimal solutions
- Implement evolutionary algorithms
- Handle optimization constraints
- Benchmark optimization algorithms
- Customize genetic operators
- Visualize optimization results
- Apply decision-making methods
- Handle mixed variable types
Inputs
- Optimization problem definition
- Algorithm configuration
- Termination criteria
- Reference directions
- Preference weights
- Benchmark problem selection
Outputs
- Optimization result object
- Decision variable values
- Objective values
- Constraint violations
- Pareto front
- Optimization visualizations
- Selected solutions
Requirements
- Python environment
- pymoo package
- NumPy support