# thieu1995/mealpy

A Collection Of The State-of-the-art Metaheuristic Algorithms In Python (Metaheuristic/Optimizer/Nature-inspired/Biology)

Repository: https://github.com/thieu1995/mealpy
Canonical: https://ross.abutalabs.com/products/mealpy
Homepage: https://mealpy.readthedocs.io
Language: Python
License: MIT
License Family: permissive
Topics: genetic-algorithm, particle-swarm-optimization, whale-optimization, grey-wolf-optimizer, harris-hawks-optimization, differential-evolution, harris-hawks-optimizationg, henry-gas-solubility-optimization, arithmetic-optimization-algorithm, equilibrium-optimizer, slime-mould-algorithm, hunger-games-search, bio-inspired-optimization, nature-inspired-algorithms, swarm-intelligence, location-optimization, production-planning, schedule-optimization, supply-chain-optimization, metaheuristics
Last push: 2026-07-24T21:22:53+00:00

## Health v2 (maintenance only)
Score: 73/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 94, release rhythm 31, longevity 100
- inputs: {"age_days": 2361, "days_push": 40, "days_rel": 382, "gap_med": 78, "n_releases_24m": 2}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1272, forks 273 (observed 2026-08-28T04:04:12.211155+00:00)

## What it is
Mealpy is a Python library providing a large collection (233+) of state-of-the-art metaheuristic optimization algorithms, including nature-inspired, bio-inspired, and evolutionary methods. It offers a simple unified interface for solving arbitrary optimization problems, analyzing convergence, and benchmarking algorithm performance.

## Use cases
- solve a black-box optimization problem in python
- run particle swarm optimization on my objective function
- compare convergence of metaheuristic algorithms
- apply genetic algorithm or differential evolution to a scheduling problem
- benchmark nature-inspired optimizers on test functions
- find a grey wolf optimizer implementation in python

## When to choose
- you need many metaheuristic optimizers behind one consistent API
- you want to benchmark or compare population-based algorithms
- you need derivative-free/global search for non-differentiable objectives

## When to avoid
- you need exact or gradient-based optimization like linear programming
- you need a commercial-grade solver with guarantees (e.g., Gurobi, CPLEX)
- your problem is small enough for scipy.optimize

## Facets
- artifact type: library
- maturity: active
- function: math, sdk, developer-tools
- domain: artificial-intelligence, data-science, mathematics
- platform: python, cross-platform
- tags: metaheuristics, optimization, nature-inspired-algorithms, swarm-intelligence, evolutionary-computation, genetic-algorithm, particle-swarm-optimization, black-box-optimization, global-optimization, algorithms

## Member repositories
- thieu1995/mealpy (main) score 73

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:12.211155+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 2026-08-30T05:03:19.193146+00:00, confidence not recorded.
  - readme: https://github.com/thieu1995/mealpy (fetched 2026-08-28T04:04:12.211155+00:00, sha 2582c7393b5e)
  - registry_pypi: https://pypi.org/pypi/mealpy/json (fetched 2026-08-29T12:15:23.024325+00:00, sha a77d78176e29)
- Data as of 2026-08-30T08:39:29.467469+00:00.