python-multiobjective-optimization
CommunityAutomate Python multiobjective optimization workflows.
System Documentation
What problem does it solve?
This Skill provides expert guidance for tackling multiobjective optimization in Python, helping you understand Pareto fronts, choose appropriate algorithms (NSGA-II, NSGA-III, MOEA/D), and implement solutions with libraries like pymoo, platypus, and DEAP. It consolidates theory and practical code to save time designing and evaluating trade-offs across conflicting objectives.
Core Features & Use Cases
- Algorithm guidance: Selection of NSGA-II, NSGA-III, MOEA/D for 2- to many-objectives.
- Pareto front analysis: Techniques to identify and interpret trade-offs.
- Library integration: How to implement with pymoo, platypus, DEAP.
- Use Case: Portfolio optimization (maximize return, minimize risk) or engineering design trade-offs.
Quick Start
Run a basic NSGA-II example with two objectives using pymoo to understand the Pareto front. For instance, set up a simple two-variable problem and run NSGA-II for 100 generations to observe the Pareto-optimal trade-offs.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: python-multiobjective-optimization Download link: https://github.com/jkitchin/skillz/archive/main.zip#python-multiobjective-optimization Please download this .zip file, extract it, and install it in the .claude/skills/ directory.