Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

A research toolkit for particle swarm optimization in Python

Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.

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

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

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

智能算法-遗传算法、蚁群算法、粒子群算法实现。实现版本Java,Python,MatLab多版本实现

🎯 A comprehensive gradient-free optimization framework written in Python

This repository implements several swarm optimization algorithms and visualizes them. Implemented algorithms: Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Cuckoo Search (CS), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA)

Autonomous car simulator (based on JavaScript & WebGL) implemented by fuzzy control system, genetic algorithm and particle swarm optimization.

The official code repository supporting the book, Grokking Artificial Intelligence Algorithms

Implement the-state-of-the-art meta-heuristic algorithms using python (numpy)

Learn about particle swarm optimization (PSO) through Python!

zoofs is a python library for performing feature selection using a variety of nature-inspired wrapper algorithms. The algorithms range from swarm-intelligence to physics-based to Evolutionary. It's easy to use , flexible and powerful tool to reduce your feature size.

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.

MoveIt kinematics_base plugin based on particle optimization & GA

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.

A (still growing) paper list of Evolutionary Computation (EC) published in some (rather all) top-tier (and also EC-focused) journals and conferences. For EC-focused publications, only Parallel/Distributed EC are covered in the current version.

记录计算智能优化算法的学习笔记，通过阅读论文并复现的形式加深对相关的启发式智能优化的理解。