# guofei9987/scikit-opt

Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)

Repository: https://github.com/guofei9987/scikit-opt
Canonical: https://ross.abutalabs.com/products/scikit-opt
Homepage: https://scikit-opt.github.io/scikit-opt/#/en/
Language: Python
License: MIT
License Family: permissive
Topics: genetic-algorithm, pso, particle-swarm-optimization, tsp, travelling-salesman-problem, simulated-annealing, ant-colony-algorithm, immune-algorithm, heuristic-algorithms, immune, artificial-intelligence, fish-swarms, optimization
Last push: 2026-03-25T09:12:13+00:00

## Health v2 (maintenance only)
Score: 56/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 74, release rhythm 8, longevity 100
- inputs: {"age_days": 3193, "days_push": 161, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 6709, forks 1107 (observed 2026-08-28T04:09:47.956742+00:00)

## What it is
A Python library implementing heuristic and swarm intelligence optimization algorithms, including genetic algorithm, particle swarm optimization, simulated annealing, ant colony, immune algorithm, artificial fish swarm, and differential evolution. It supports user-defined operators and includes TSP (traveling salesman problem) solving.

## Use cases
- solve traveling salesman problem in python
- run a genetic algorithm on my objective function
- optimize parameters with particle swarm optimization
- apply simulated annealing to minimize a function
- use ant colony optimization for routing
- try differential evolution for global optimization
- customize selection and crossover operators in a genetic algorithm

## When to choose
- you need a variety of metaheuristic optimization algorithms behind one scikit-learn-like Python API
- you want to plug in custom selection, crossover, or mutation operators
- you need quick TSP or combinatorial optimization prototyping

## When to avoid
- you need large-scale, GPU-accelerated, or production-grade optimization performance
- you need exact/linear programming solvers rather than heuristics
- you need deep learning or gradient-based training tools

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, simulation, math
- domain: artificial-intelligence, performance, data-science
- platform: python, windows
- tags: genetic-algorithm, particle-swarm-optimization, simulated-annealing, ant-colony, heuristic-optimization, tsp, differential-evolution, swarm-intelligence, algorithms, linux, macos

## Member repositories
- guofei9987/scikit-opt (main) score 56

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:47.956742+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:42:56.955540+00:00, confidence not recorded.
  - readme: https://github.com/guofei9987/scikit-opt (fetched 2026-08-28T04:09:47.956742+00:00, sha c7175cc0ca16)
  - homepage: https://scikit-opt.github.io/scikit-opt/#/en/ (fetched 2026-08-29T08:39:00.827704+00:00, sha 0c5314e2b60e)
  - registry_pypi: https://pypi.org/pypi/scikit-opt/json (fetched 2026-08-29T08:39:00.830127+00:00, sha 298e19ad989f)
- Data as of 2026-08-30T08:39:29.467469+00:00.