pymoo

1# Pymoo - Multi-Objective Optimization in Python
2 
3## Overview
4 
5Pymoo 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.
6 
7## When to Use This Skill
8 
9This skill should be used when:
10- Solving optimization problems with one or multiple objectives
11- Finding Pareto-optimal solutions and analyzing trade-offs
12- Implementing evolutionary algorithms (GA, DE, PSO, NSGA-II/III)
13- Working with constrained optimization problems
14- Benchmarking algorithms on standard test problems (ZDT, DTLZ, WFG)
15- Customizing genetic operators (crossover, mutation, selection)
16- Visualizing high-dimensional optimization results
17- Making decisions from multiple competing solutions
18- Handling binary, discrete, continuous, or mixed-variable problems
19 
20## Core Concepts
21 
22### The Unified Interface
23 
24Pymoo uses a consistent `minimize()` function for all optimization tasks:
25 
26```python
27from pymoo.optimize import minimize
28 
29result = minimize(
30    problem,        # What to optimize
31    algorithm,      # How to optimize
32    termination,    # When to stop
33    seed=1,
34    verbose=True
35)
36```
37 
38**Result object contains:**
39- `result.X`: Decision variables of optimal solution(s)
40…