swarm-intelligence

A research toolkit for particle swarm optimization in Python

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

🐝 GPTSwarm: LLM agents as (Optimizable) Graphs

OpenHuFu is an open-sourced data federation system to support collaborative queries over multi databases with security guarantee.

EvoloPy toolbox provides classical and recent nature-inspired metaheuristic for the global optimization.

Protein-protein, protein-peptide and protein-DNA docking framework based on the GSO algorithm

Neural Architecture Search Powered by Swarm Intelligence 🐜

Python microframework for building nature-inspired algorithms. Official docs: https://niapy.org

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.

Swarming algorithms like PSO, Ant Colony, Sakana, and more in PyTorch 😊

The goal of this repository is to introduce a new, customizable, scalable, and fully opensource mobile robot platform, called SMARTmBOT. This repository provides a guide, and all design files and source codes so that you can build your own SMARTmBOT. SMARTmBOT can be useful for studying the basics of robotics, especially mobile robotics. It can also be used to study advanced topics such as swarm robotics.

Python implementation of QBSO-FS : a Reinforcement Learning based Bee Swarm Optimization metaheuristic for Feature Selection problem.

Implementation of Firefly Algorithm in Python

This is a Boids Simulation, written in Python with Pygame.

Solving Travelling Salesman Problem using Ant Colony Optimization

Ant colony simulator

Digimon Engine — Multi-Agent, Multi-Player Framework for AI-Native Games and Agentic Metaverse

In this paper, a new stochastic optimizer, which is called slime mould algorithm (SMA), is proposed based upon the oscillation mode of slime mould in nature. The proposed SMA has several new features with a unique mathematical model that uses adaptive weights to simulate the process of producing positive and negative feedback of the propagation wave of slime mould based on bio-oscillator to form the optimal path for connecting food with excellent exploratory ability and exploitation propensity. The proposed SMA is compared with up-to-date metaheuristics in an extensive set of benchmarks to verify the efficiency. Moreover, four classical engineering structure problems are utilized to estimate the efficacy of the algorithm in optimizing engineering problems. The results demonstrate that the algorithm proposed benefits from competitive, often outstanding performance on different search landscapes. The source codes and info of SMA are publicly available at: http://www.alimirjalili.com/SMA.html

Exploration into the Firefly algorithm in Pytorch

Mobile Robot Path Planning and Obstacle Avoidance Using PSO in Python