Agent skill · research science · k-dense-ai
pymoo
Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.
Why this skill is useful
Provides domain-specific knowledge on multi-objective optimization techniques and algorithms that the AI wouldn't reliably generate on its own.
What it needs
Requires numpy, pymoo, scipy installed locally. About 6k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
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, SPEA2), 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. Current stable release: pymoo 0.6.1.6 (November 2025). Installation For reproducible environments, pin a version: uv pip install "pymoo==0.6.1.6". Dependencies: NumPy (2.x compatible since 0.6.1.3), SciPy, matplotlib (visualization). Autograd is optional for gradient-based features (since 0.6.1.3). Documentation: https://pymoo.org/ — LLM-friendly index: https://pymoo.org/llms.txt 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: 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 Definition Styles Pymoo supports three problem definition styles: Problem: Vectorized — evaluate receives a batch of solutions (matrix) ElementwiseProblem: One solution per call — recommended for custom problems and paralle …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/pymoo