Can this skill handle constrained optimization?

Yes, it supports a variety of constraint handling methods including feasibility-first, penalty methods, and treating constraints as objectives.

Does it support decision making after finding solutions?

Yes, the skill includes Multi-Criteria Decision Making (MCDM) tools like Pseudo-Weights and Compromise Programming to help select the best single solution from a Pareto front.

What is Pymoo used for in Claude Code?

Pymoo is used to solve optimization problems where you have one or more objectives to minimize or maximize, particularly when those objectives conflict with one another.

Which algorithm should I use for 5 objectives?

For many-objective problems (4 or more), NSGA-III is the recommended choice as it uses reference directions to maintain population diversity.

Pymoo Optimization Assistant

Name: Pymoo Optimization Assistant
Author: ricable

byricable

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Ciencia de Datos y ML

Solves complex single, multi, and many-objective optimization problems using evolutionary algorithms and Pareto-optimal analysis.

Acerca de

This skill empowers Claude to leverage the Pymoo Python framework for solving sophisticated mathematical and engineering optimization tasks. It provides domain-specific expertise in implementing state-of-the-art algorithms like NSGA-II, NSGA-III, and MOEA/D to find trade-off solutions for conflicting objectives. By incorporating advanced constraint handling, multi-criteria decision making (MCDM), and specialized visualization techniques, this skill enables the analysis of high-dimensional Pareto fronts and the selection of optimal design solutions in complex problem spaces.

Características Principales

Implementation of multi-objective algorithms including NSGA-II, NSGA-III, and MOEA/D
Access to standard benchmark problem suites like ZDT, DTLZ, and WFG
Advanced constraint handling for equality and inequality problem formulations
Multi-criteria decision making (MCDM) for selecting preferred Pareto solutions
1 GitHub stars
High-dimensional visualization via Scatter, 3D, and Parallel Coordinate Plots

Casos de Uso

Automating complex hyperparameter tuning for machine learning pipelines
Resource allocation and scheduling problems with multiple competing constraints
Engineering design optimization where performance, cost, and weight are in conflict

Acerca de

Características Principales

Implementation of multi-objective algorithms including NSGA-II, NSGA-III, and MOEA/D
Access to standard benchmark problem suites like ZDT, DTLZ, and WFG
Advanced constraint handling for equality and inequality problem formulations
Multi-criteria decision making (MCDM) for selecting preferred Pareto solutions
1 GitHub stars
High-dimensional visualization via Scatter, 3D, and Parallel Coordinate Plots

Casos de Uso

Automating complex hyperparameter tuning for machine learning pipelines
Resource allocation and scheduling problems with multiple competing constraints
Engineering design optimization where performance, cost, and weight are in conflict