Agent skill · research science · aiskillstore
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 and algorithms for multi-objective optimization that the AI wouldn't reliably generate on its own.
What it needs
Requires matplotlib, numpy, python installed locally. About 8k tokens when loaded. Last updated 2026-08-07. 405 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), 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: 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 Steps: 1. Define or select problem 2. Choose single-objective algorithm (GA, DE, PSO, CMA-ES) 3. Configure termination criteria 4. Run optimization 5. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/pymoo