thieu1995/mealpy

Health v2 · maintenance only

Adoption not part of the score

1272 stars · 273 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

library · maturity active

math sdk developer-tools artificial-intelligence data-science mathematics python cross-platform metaheuristics optimization nature-inspired-algorithms swarm-intelligence evolutionary-computation genetic-algorithm particle-swarm-optimization black-box-optimization global-optimization algorithms

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