# ljvmiranda921/pyswarms

A research toolkit for particle swarm optimization in Python

Repository: https://github.com/ljvmiranda921/pyswarms
Canonical: https://ross.abutalabs.com/products/pyswarms
Homepage: https://pyswarms.readthedocs.io/en/latest/
Language: Python
License: MIT
License Family: permissive
Topics: particle-swarm-optimization, optimization-tools, pso, global-optimization, swarm-intelligence, machine-learning, discrete-optimization, optimization, optimization-algorithms, metaheuristics, algorithm
Last push: 2024-08-06T17:18:34+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3339, "days_push": 757, "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 1393, forks 337 (observed 2026-08-28T04:04:36.304613+00:00)

## What it is
PySwarms is a Python research toolkit for particle swarm optimization (PSO), offering a high-level declarative interface with built-in optimizers, objective functions, plotting, and hyperparameter search tools. It targets swarm intelligence researchers, practitioners, and students, though the maintainer has stopped active development and recommends alternatives like scikit-opt.

## Use cases
- optimize a continuous objective function with particle swarm optimization
- run PSO for hyperparameter search in machine learning models
- visualize swarm movement and cost history during optimization
- implement and test custom swarm intelligence algorithms in Python
- solve global optimization problems with metaheuristics
- teach or study swarm intelligence with a high-level PSO library

## When to choose
- you need a simple, well-documented Python PSO library for research or teaching
- you want built-in plotting and objective functions for swarm optimization experiments
- you prefer a high-level declarative API over writing PSO from scratch

## When to avoid
- you need actively maintained software with recent bug fixes and new Python version support
- you need a broader set of metaheuristics beyond PSO (consider scikit-opt)
- you require production-grade optimization performance at scale

## Facets
- artifact type: library
- maturity: abandoned
- function: machine-learning, math, data-science
- domain: machine-learning, data-science
- platform: python, cross-platform
- tags: particle-swarm-optimization, pso, swarm-intelligence, metaheuristics, global-optimization, optimization, algorithms

## Member repositories
- ljvmiranda921/pyswarms (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:36.304613+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 2026-08-30T04:39:29.480267+00:00, confidence not recorded.
  - readme: https://github.com/ljvmiranda921/pyswarms (fetched 2026-08-28T04:04:36.304613+00:00, sha 28fba75de17d)
  - registry_pypi: https://pypi.org/pypi/pyswarms/json (fetched 2026-08-29T11:54:50.759840+00:00, sha 6ada3d8bad01)
- Data as of 2026-08-30T08:39:29.467469+00:00.