Pymoo

Pymoo - Multi-Objective Optimization in Python

Overview

Pymoo is a comprehensive Python framework for optimization with emphasis on multi-objective problems. Solve single and multi-objective optimization using state-of-the-art algorithms (NSGA-II/III, MOEA/D), benchmark problems (ZDT, DTLZ), customizable genetic operators, and multi-criteria decision making methods. Excels at finding trade-off solutions (Pareto fronts) for problems with conflicting objectives.

When to Use This Skill

This skill should be used when:

• Solving optimization problems with one or multiple objectives
• Finding Pareto-optimal solutions and analyzing trade-offs
• Implementing evolutionary algorithms (GA, DE, PSO, NSGA-II/III)
• Working with constrained optimization problems
• Benchmarking algorithms on standard test problems (ZDT, DTLZ, WFG)
• Customizing genetic operators (crossover, mutation, selection)
• Visualizing high-dimensional optimization results
• Making decisions from multiple competing solutions
• Handling binary, discrete, continuous, or mixed-variable problems

Core Concepts

The Unified Interface

Pymoo uses a consistent minimize() function for all optimization tasks:

from pymoo.optimize import minimize

result = minimize(
    problem,        # What to optimize
    algorithm,      # How to optimize
    termination,    # When to stop
    seed=1,
    verbose=True
)

Result object contains:

• result.X: Decision variables of optimal solution(s)
• result.F: Objective values of optimal solution(s)
• result.G: Constraint violations (if constrained)
• result.algorithm: Algorithm object with history

Problem Types

Single-objective: One objective to minimize/maximize Multi-objective: 2-3 conflicting objectives → Pareto front Many-objective: 4+ objectives → High-dimensional Pareto front Constrained: Objectives + inequality/equality constraints Dynamic: Time-varying objectives or constraints

Quick Start Workflows

Workflow 1: Single-Objective Optimization

When: Optimizing one objective function. Algorithms: GA, DE, PSO, CMA-ES.

from pymoo.algorithms.soo.nonconvex.ga import GA
from pymoo.problems import get_problem
from pymoo.optimize import minimize

# Built-in problem
problem = get_problem("rastrigin", n_var=10)

# Configure Genetic Algorithm
algorithm = GA(
    pop_size=100,
    eliminate_duplicates=True
)

# Optimize
result = minimize(
    problem,
    algorithm,
    ('n_gen', 200),
    seed=1,
    verbose=True
)

print(f"Best solution: {result.X}")
print(f"Best objective: {result.F[0]}")

See: scripts/single_objective_example.py for complete example

Workflow 2: Multi-Objective Optimization (2-3 objectives)

When: Optimizing 2-3 conflicting objectives, need Pareto front. Algorithm: NSGA-II (standard for bi/tri-objective).

from pymoo.algorithms.moo.nsga2 import NSGA2
from pymoo.problems import get_problem
from pymoo.optimize import minimize
from pymoo.visualization.scatter import Scatter

# Bi-objective benchmark problem
problem = get_problem("zdt1")

# NSGA-II algorithm
algorithm = NSGA2(pop_size=100)

# Optimize
result = minimize(problem, algorithm, ('n_gen', 200), seed=1)

# Visualize Pareto front
plot = Scatter()
plot.add(result.F, label="Obtained Front")
plot.add(problem.pareto_front(), label="True Front", alpha=0.3)
plot.show()

print(f"Found {len(result.F)} Pareto-optimal solutions")

See: scripts/multi_objective_example.py for complete example

Workflow 3: Many-Objective Optimization (4+ objectives)

When: Optimizing 4+ objectives. Algorithm: NSGA-III. Key requirement: must supply reference directions for population guidance.

from pymoo.algorithms.moo.nsga3 import NSGA3
from pymoo.problems import get_problem
from pymoo.optimize import minimize
from pymoo.util.ref_dirs import get_reference_directions
from pymoo.visualization.pcp import PCP

# Many-objective problem (5 objectives)
problem = get_problem("dtlz2", n_obj=5)

# Generate reference directions (required for NSGA-III)
ref_dirs = get_reference_directions("das-dennis", n_dim=5, n_partitions=12)

# Configure NSGA-III
algorithm = NSGA3(ref_dirs=ref_dirs)

# Optimize
result = minimize(problem, algorithm, ('n_gen', 300), seed=1)

# Visualize with Parallel Coordinates
plot = PCP(labels=[f"f{i+1}" for i in range(5)])
plot.add(result.F, alpha=0.3)
plot.show()

See: scripts/many_objective_example.py for complete example

Workflow 4: Custom Problem Definition

When: Solving a domain-specific problem. Extend ElementwiseProblem, set dimensions/bounds in __init__, implement _evaluate for objectives (and constraints).

Unconstrained example:

from pymoo.core.problem import ElementwiseProblem
import numpy as np

class MyProblem(ElementwiseProblem):
    def __init__(self):
        super().__init__(
            n_var=2,              # Number of variables
            n_obj=2,              # Number of objectives
            xl=np.array([0, 0]),  # Lower bounds
            xu=np.array([5, 5])   # Upper bounds
        )

    def _evaluate(self, x, out, *args, **kwargs):
        # Define objectives
        f1 = x[0]**2 + x[1]**2
        f2 = (x[0]-1)**2 + (x[1]-1)**2

        out["F"] = [f1, f2]

Constrained example:

class ConstrainedProblem(ElementwiseProblem):
    def __init__(self):
        super().__init__(
            n_var=2,
            n_obj=2,
            n_ieq_constr=2,        # Inequality constraints
            n_eq_constr=1,         # Equality constraints
            xl=np.array([0, 0]),
            xu=np.array([5, 5])
        )

    def _evaluate(self, x, out, *args, **kwargs):
        # Objectives
        out["F"] = [f1, f2]

        # Inequality constraints (g <= 0)
        out["G"] = [g1, g2]

        # Equality constraints (h = 0)
        out["H"] = [h1]

Constraint formulation rules:

• Inequality: Express as g(x) <= 0 (feasible when ≤ 0)
• Equality: Express as h(x) = 0 (feasible when = 0)
• Convert g(x) >= b to -(g(x) - b) <= 0

See: scripts/custom_problem_example.py for complete examples

Workflow 5: Constraint Handling

When: Problem has feasibility constraints. Four approaches:

1. Feasibility First (Default - Recommended)

from pymoo.algorithms.moo.nsga2 import NSGA2

# Works automatically with constrained problems
algorithm = NSGA2(pop_size=100)
result = minimize(problem, algorithm, termination)

# Check feasibility
feasible = result.CV[:, 0] == 0  # CV = constraint violation
print(f"Feasible solutions: {np.sum(feasible)}")

2. Penalty Method

from pymoo.constraints.as_penalty import ConstraintsAsPenalty

# Wrap problem to convert constraints to penalties
problem_penalized = ConstraintsAsPenalty(problem, penalty=1e6)

3. Constraint as Objective

from pymoo.constraints.as_obj import ConstraintsAsObjective

# Treat constraint violation as additional objective
problem_with_cv = ConstraintsAsObjective(problem)

4. Specialized Algorithms

from pymoo.algorithms.soo.nonconvex.sres import SRES

# SRES has built-in constraint handling
algorithm = SRES()

See: references/constraints_mcdm.md for comprehensive constraint handling guide

Workflow 6: Decision Making from Pareto Front

When: Have a Pareto front, need to select preferred solution(s). Pipeline: optimize → normalize objectives → set weights → apply MCDM.

Example using Pseudo-Weights:

from pymoo.mcdm.pseudo_weights import PseudoWeights
import numpy as np

# After obtaining result from multi-objective optimization
# Normalize objectives
F_norm = (result.F - result.F.min(axis=0)) / (result.F.max(axis=0) - result.F.min(axis=0))

# Define preferences (must sum to 1)
weights = np.array([0.3, 0.7])  # 30% f1, 70% f2

# Apply decision making
dm = PseudoWeights(weights)
selected_idx = dm.do(F_norm)

# Get selected solution
best_solution = result.X[selected_idx]
best_objectives = result.F[selected_idx]

print(f"Selected solution: {best_solution}")
print(f"Objective values: {best_objectives}")

Other MCDM methods:

• Compromise Programming: Select closest to ideal point
• Knee Point: Find balanced trade-off solutions
• Hypervolume Contribution: Select most diverse subset

See:

• scripts/decision_making_example.py for complete example
• references/constraints_mcdm.md for detailed MCDM methods

Workflow 7: Visualization

Choose visualization based on number of objectives:

2 objectives: Scatter Plot

from pymoo.visualization.scatter import Scatter

plot = Scatter(title="Bi-objective Results")
plot.add(result.F, color="blue", alpha=0.7)
plot.show()

3 objectives: 3D Scatter

plot = Scatter(title="Tri-objective Results")
plot.add(result.F)  # Automatically renders in 3D
plot.show()

4+ objectives: Parallel Coordinate Plot

from pymoo.visualization.pcp import PCP

plot = PCP(
    labels=[f"f{i+1}" for i in range(n_obj)],
    normalize_each_axis=True
)
plot.add(result.F, alpha=0.3)
plot.show()

Solution comparison: Petal Diagram

from pymoo.visualization.petal import Petal

plot = Petal(
    bounds=[result.F.min(axis=0), result.F.max(axis=0)],
    labels=["Cost", "Weight", "Efficiency"]
)
plot.add(solution_A, label="Design A")
plot.add(solution_B, label="Design B")
plot.show()

See: references/visualization.md for all visualization types and usage

Algorithm Selection Guide

Single-Objective Problems

Algorithm	Best For	Key Features
GA	General-purpose	Flexible, customizable operators
DE	Continuous optimization	Good global search
PSO	Smooth landscapes	Fast convergence
CMA-ES	Difficult/noisy problems	Self-adapting

Multi-Objective Problems (2-3 objectives)

Algorithm	Best For	Key Features
NSGA-II	Standard benchmark	Fast, reliable, well-tested
R-NSGA-II	Preference regions	Reference point guidance
MOEA/D	Decomposable problems	Scalarization approach

Many-Objective Problems (4+ objectives)

Algorithm	Best For	Key Features
NSGA-III	4-15 objectives	Reference direction-based
RVEA	Adaptive search	Reference vector evolution
AGE-MOEA	Complex landscapes	Adaptive geometry

Constrained Problems

Approach	Algorithm	When to Use
Feasibility-first	Any algorithm	Large feasible region
Specialized	SRES, ISRES	Heavy constraints
Penalty	GA + penalty	Algorithm compatibility

See: references/algorithms.md for comprehensive algorithm reference

Benchmark Problems

Quick problem access:

from pymoo.problems import get_problem

# Single-objective
problem = get_problem("rastrigin", n_var=10)
problem = get_problem("rosenbrock", n_var=10)

# Multi-objective
problem = get_problem("zdt1")        # Convex front
problem = get_problem("zdt2")        # Non-convex front
problem = get_problem("zdt3")        # Disconnected front

# Many-objective
problem = get_problem("dtlz2", n_obj=5, n_var=12)
problem = get_problem("dtlz7", n_obj=4)

See: references/problems.md for complete test problem reference

Genetic Operator Customization

Standard operator configuration:

from pymoo.algorithms.soo.nonconvex.ga import GA
from pymoo.operators.crossover.sbx import SBX
from pymoo.operators.mutation.pm import PM

algorithm = GA(
    pop_size=100,
    crossover=SBX(prob=0.9, eta=15),
    mutation=PM(eta=20),
    eliminate_duplicates=True
)

Operator selection by variable type:

Continuous variables:

• Crossover: SBX (Simulated Binary Crossover)
• Mutation: PM (Polynomial Mutation)

Binary variables:

• Crossover: TwoPointCrossover, UniformCrossover
• Mutation: BitflipMutation

Permutations (TSP, scheduling):

• Crossover: OrderCrossover (OX)
• Mutation: InversionMutation

See: references/operators.md for comprehensive operator reference

Performance and Troubleshooting

Symptom	Fixes
Not converging	Increase pop size / generations; try different algorithm (multimodal); verify constraint formulation
Poor Pareto distribution	Adjust reference directions (NSGA-III); larger pop; enable duplicate elimination; check scaling
Few feasible solutions	Constraint-as-objective; repair operators; SRES/ISRES; verify g(x) <= 0 formulation
High compute cost	Smaller pop / fewer generations; simpler operators; parallelize if supported

Best practices

1. Normalize objectives when scales differ significantly
2. Set seed for reproducibility
3. save_history=True to analyze convergence
4. Visualize results to understand solution quality
5. Compare with true Pareto front when available
6. Pick appropriate termination (('n_gen', N), evaluations, tolerance)
7. Tune operator parameters for problem characteristics

Resources

This skill includes comprehensive reference documentation and executable examples:

references/

Detailed documentation for in-depth understanding:

• algorithms.md: Complete algorithm reference with parameters, usage, and selection guidelines
• problems.md: Benchmark test problems (ZDT, DTLZ, WFG) with characteristics
• operators.md: Genetic operators (sampling, selection, crossover, mutation) with configuration
• visualization.md: All visualization types with examples and selection guide
• constraints_mcdm.md: Constraint handling techniques and multi-criteria decision making methods

Search patterns for references:

• Algorithm details: grep -r "NSGA-II\|NSGA-III\|MOEA/D" references/
• Constraint methods: grep -r "Feasibility First\|Penalty\|Repair" references/
• Visualization types: grep -r "Scatter\|PCP\|Petal" references/

scripts/

Executable examples demonstrating common workflows:

• single_objective_example.py: Basic single-objective optimization with GA
• multi_objective_example.py: Multi-objective optimization with NSGA-II, visualization
• many_objective_example.py: Many-objective optimization with NSGA-III, reference directions
• custom_problem_example.py: Defining custom problems (constrained and unconstrained)
• decision_making_example.py: Multi-criteria decision making with different preferences

Run examples:

python3 scripts/single_objective_example.py
python3 scripts/multi_objective_example.py
python3 scripts/many_objective_example.py
python3 scripts/custom_problem_example.py
python3 scripts/decision_making_example.py

Additional Notes

Install: uv pip install pymoo (deps: NumPy, SciPy, matplotlib; optional autograd for gradient-based). Docs: https://pymoo.org/. Target version: pymoo 0.6.x.

Common patterns:

• Use ElementwiseProblem for custom problems
• Constraints: g(x) <= 0 and h(x) = 0; convert g(x) >= b via -(g(x) - b) <= 0
• NSGA-III requires reference directions
• Normalize objectives before MCDM
• Termination: ('n_gen', N) or get_termination("f_tol", tol=0.001)