pymoo
Optimize Multi-Objective Problems with pymoo
Conflicting objectives make algorithm selection, constraint handling, and trade-off analysis difficult. This skill provides practical pymoo workflows, examples, and reference guidance.
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Review the Skillstore skill "pymoo" from https://skillstore.io/skills/davila7-pymoo.md and its manifest at https://skillstore.io/api/skills/davila7-pymoo/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "pymoo". Select an algorithm for three continuous objectives with inequality constraints.
Expected outcome:
Start with NSGA-II and feasibility-first handling. Use a moderate population, fixed seed, normalized objectives, and track feasible Pareto diversity.
Using "pymoo". Explain why a many-objective result has poor coverage.
Expected outcome:
- Check whether reference directions match the objective count.
- Increase evaluations while holding the random seed strategy constant.
- Normalize objectives and inspect duplicate or infeasible solutions.
Using "pymoo". Choose one design from a two-objective Pareto front.
Expected outcome:
Normalize both objectives, document preference weights, apply pseudo-weights, and compare the selected design with both extreme solutions.
Security Audit
SafeAll 70 static findings are false positives caused by Markdown formatting, benign fixed commands, and a documentation link. No prompt injection, data exfiltration, unsafe command construction, or malicious intent was found.
Risk Factors
⚙️ External commands (50)
🌐 Network access (1)
Share & cite this report
Share the versioned assessment report, neutral badge, embed card, and citations. Skillstore reports evidence without deciding whether this Skill is safe.
Copy report link
https://skillstore.io/skills/davila7-pymoo/audits/9?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportMarkdown badge
[](https://skillstore.io/skills/davila7-pymoo?utm_source=security_passport_badge)HTML badge
<a href="https://skillstore.io/skills/davila7-pymoo?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/davila7-pymoo/security.svg" alt="Skillstore security assessment" loading="lazy"></a>Embed card
<iframe src="https://skillstore.io/embed/skills/davila7-pymoo.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA · BibTeX · CFF)
APA citation
davila7. (2026). pymoo security audit report (audit version 9) [Author version unspecified]. Skillstore. https://skillstore.io/skills/davila7-pymoo/audits/9BibTeX citation
@techreport{davila7-davila7-pymoo-2026,
author = {davila7},
title = {pymoo security audit report (audit version 9)},
institution = {Skillstore},
year = {2026},
number = {9},
url = {https://skillstore.io/skills/davila7-pymoo/audits/9},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "pymoo security audit report (audit version 9)"
version: "unspecified"
type: report
authors:
- name: "davila7"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/davila7-pymoo/audits/9"
identifiers:
- type: other
value: "skillstore:davila7-pymoo:audit:9"
description: "Skillstore immutable audit report identifier"
Compare variants
2 installable variantsEach author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
davila7-pymoo
2026-08-21
k-dense-ai-pymoo
2026-08-21
Skillstore Score
Why this score Evidence Confidence: HighWhat You Can Build
Compare Engineering Designs
Model cost, weight, and performance objectives, then inspect feasible Pareto-optimal designs.
Benchmark Evolutionary Algorithms
Run reproducible comparisons on ZDT or DTLZ problems using suitable algorithms and termination criteria.
Teach Pareto Optimization
Use focused examples to explain conflicting objectives, constraints, Pareto fronts, and preference-based selection.
Try These Prompts
Recommend a pymoo algorithm for [objective count], [variable type], and [constraints]. Explain the choice and suggest initial population and termination settings.
Design a pymoo ElementwiseProblem for [objectives], [bounds], and [constraints]. Check objective direction and convert inequalities to the required form.
Review these convergence statistics and Pareto results: [results]. Identify scaling, feasibility, diversity, or termination issues and recommend controlled experiments.
Create a reproducible comparison of [algorithms] on [problem]. Define seeds, budgets, indicators, constraint metrics, visualizations, and decision criteria.
Best Practices
- Set random seeds and compare multiple runs before drawing conclusions.
- Normalize objectives when scales differ and report feasibility separately.
- Match algorithms, operators, and termination criteria to variable and objective structure.
Avoid
- Do not treat one stochastic run as reliable evidence.
- Do not compare algorithms with different evaluation budgets.
- Do not select a Pareto solution before documenting stakeholder preferences.
Frequently Asked Questions
Does this skill support single-objective optimization?
Which algorithm should I use for two objectives?
How are inequality constraints represented?
Can it handle five or more objectives?
Are the included examples production-ready?
Does the skill install pymoo automatically?
Developer Details
Author
davila7License
MIT
Skillstore revision
r2
Version notice
The author did not declare a version.
Ref
c43861a65bb95efcae259cd161c9d6f4dc7eec6f
Maintenance freshness
7/24/2026
Usage
7 downloads · 495 views