pymoo
Solves single- and multi-objective optimization problems in Python with pymoo, using NSGA-II, NSGA-III, MOEA/D, SPEA2, RVEA, GA, DE and PSO. Covers custom problems (Problem, ElementwiseProblem, FunctionalProblem), constraint handling, mixed-variable problems, ZDT/DTLZ/WFG benchmarks, genetic operators, parallel evaluation, Pareto front visualization and multi-criteria decision making. Use when finding Pareto-optimal trade-offs between conflicting objectives. Use when defining a constrained or mixed-variable problem and choosing an evolutionary algorithm. Use when benchmarking algorithms on standard test problems. Use when customizing crossover or mutation operators. Use when picking one solution from a Pareto front. Not for gradient-based or convex solvers.
- Version
- 1.4
- License
- Apache-2.0 license
- Compatibility
- Requires Python 3.10+ and pymoo (uv pip install). Optional matplotlib for visualization plots; optional autograd for gradient-based features; optional joblib for JoblibParallelization.
Pinned to revision df088027ff23, so it is the text this page describes rather than whatever the author pushed since.
Pre-approved tools experimental
Experimental field. Support varies between clients, so this list is what the author declared, not what your client will enforce.
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Files
- skills/pymoo/SKILL.md
- skills/pymoo/references/algorithms.md
- skills/pymoo/references/constraints_mcdm.md
- skills/pymoo/references/operators.md
- skills/pymoo/references/parallelization.md
- skills/pymoo/references/problems.md
- skills/pymoo/references/quick_start_workflows.md
- skills/pymoo/references/visualization.md
- skills/pymoo/scripts/custom_problem_example.py
- skills/pymoo/scripts/decision_making_example.py
- skills/pymoo/scripts/many_objective_example.py
- skills/pymoo/scripts/multi_objective_example.py
- skills/pymoo/scripts/single_objective_example.py
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