thieu1995/mealpy

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

What it solves

MEALPY is a comprehensive Python library designed to solve complex optimization problems using a vast collection of meta-heuristic algorithms. It provides a standardized way to apply population-based metaheuristics (PBMs) for continuous, discrete, and combinatorial optimization, including tasks like hyper-parameter tuning and feature selection in AI/ML models.

How it works

The library implements over 230 nature-inspired and bio-inspired algorithms (such as Particle Swarm Optimization, Genetic Algorithms, and various swarm intelligence methods). It allows users to define their search space using specific decision variables (e.g., FloatVar, IntegerVar, BoolVar, CategoricalVar) and then apply an optimizer to find the best solution for a given objective function.

Who it’s for

It is primarily aimed at researchers and developers who need to perform global search optimization, analyze the convergence and robustness of different algorithms, or optimize parameters for machine learning and AI systems.

Highlights

  • Extensive Algorithm Library: Contains 233 algorithms, including original, hybrid, and variant versions.
  • Flexible Variable Types: Supports a wide range of decision variables including float, integer, string, binary, boolean, permutation, categorical, and sequence variables.
  • Analysis Tools: Built-in capabilities to analyze parameter performance, convergence rates, and scalability.
  • Citations and Ethics: Provides clear guidance on scientific integrity, specifically flagging algorithms suspected of plagiarism to help researchers maintain credibility.

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