NSGA2, NSGA3, R-NSGA3, MOEAD, Genetic Algorithms (GA), Differential Evolution (DE), CMAES, PSO

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

🍀 Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution)

OptimLib: a lightweight C++ library of numerical optimization methods for nonlinear functions

Evolutionary & genetic algorithms for Julia

This toolbox offers 13 wrapper feature selection methods (PSO, GA, GWO, HHO, BA, WOA, and etc.) with examples. It is simple and easy to implement.

High-performance metaheuristics for optimization coded purely in Julia.

[JMLR-2024] PyPop7: A Pure-Python Library for POPulation-based Black-Box Optimization (BBO), especially *Large-Scale* variants (including evolutionary algorithms, swarm-based optimizers, pattern search, and random search). [https://jmlr.org/papers/v25/23-0386.html (CCF-A)] (Planned Extensions: PyCoPop7, PyNoPop7, PyPop77, and PyMePop7)

A C++ library of Markov Chain Monte Carlo (MCMC) methods

This toolbox offers more than 40 wrapper feature selection methods include PSO, GA, DE, ACO, GSA, and etc. They are simple and easy to implement.

Derivative-Free Global Optimization Algorithm (C++, Python binding) - Continuous, Discrete, TSP, NLS, MINLP

Yet another black-box optimization library for Python

Python library for stochastic numerical optimization

Heuristic Optimization for Python

Examples on numerical optimization

Tuning the Parameters of Heuristic Optimizers (Meta-Optimization / Hyper-Parameter Optimization)

A simple, bare bones, implementation of differential evolution optimization.

Yarpiz Evolutionary Algorithms Toolbox for MATLAB

Density evolution for LDPC codes construction under AWGN-channel: reciprocal-channel approximation (RCA), Gaussian Evolution, Covariance Evolution

Functions, examples and data from the first and the second edition of "Numerical Methods and Optimization in Finance" by M. Gilli, D. Maringer and E. Schumann (2019, ISBN:978-0128150658). This repository mirrors https://gitlab.com/NMOF/NMOF .