pymoo
Category: data-science-and-ml · License: Apache-2.0 license · Version: 1.4 · Includes scripts
Tags: multi-objective optimization, evolutionary algorithms, NSGA-II, optimization
When to use it
Solves single- and multi-objective optimization problems in Python with pymoo, using NSGA-II, NSGA-III, MOEA/D, SPEA2, RVEA, GA, DE and PSO. Covers custom problems (Problem, ElementwiseProblem, FunctionalProblem), constraint handling, mixed-variable problems, ZDT/DTLZ/WFG benchmarks, genetic operators, parallel evaluation, Pareto front visualization and multi-criteria decision making. Use when finding Pareto-optimal trade-offs between conflicting objectives. Use when defining a constrained or mixed-variable problem and choosing an evolutionary algorithm. Use when benchmarking algorithms on standard test problems. Use when customizing crossover or mutation operators. Use when picking one solution from a Pareto front. Not for gradient-based or convex solvers.Example requests
Ask your agent in plain language, for example:- “How do I get the Pareto front for two conflicting objectives in Python?”
- “I’m designing a truss in truss_opt.py. It has 3 variables: a continuous beam thickness, an integer number of struts, and a categorical material (steel, aluminum, titanium). I’m minimizing mass and deflection. Stress must stay under 250 MPa and the deflection limit is a hard constraint. My NSGA-II run keeps crashing with ‘ValueError: could not convert string to float’ when the material gets sampled. How should I set this up?”
- “I have a simulation of a bioreactor with 5 competing goals: yield, cost, energy use, waste and run time. I want a spread of good trade-off designs. Then I need to pick a single recipe to run in the lab, and I care most about yield. How should I go about this?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole data-science-and-ml category with /plugin install data-science-and-ml@research-agent-skills.