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14 search results found

Pyswarms ⭐ 1,154

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)

Evolopy ⭐ 331

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

Fireflyalgorithm ⭐ 53

Implementation of Firefly Algorithm in Python

Swarmopt ⭐ 32

Swarm intelligence optimizer

Harris Hawks Optimization Algorithm And Applications ⭐ 25

Source codes for HHO paper: Harris hawks optimization: Algorithm and applications: https://www.sciencedirect.com/science/article/pii/ In this paper, a novel population-based, nature-inspired optimization paradigm is proposed, which is called Harris Hawks Optimizer (HHO).

This repository include implementation of particle swarm optimization (pso) algorithm in C++

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

Particle Swarm Optimization Fortran 95 ⭐ 11

A Fortran 95 code for Particle Swarm Optimization. The code is general. The fitness function is defined in a separate file and can be replaced by any user defined fitness function.

Particle_swarm_intelligence ⭐ 11

Project: An efficient approach to find the optimal network route using Swarm Intelligence.

Spidermonkey.jl ⭐ 9

A Julia package for Spider Monkey Optimization.

Slime Mould Algorithm A New Method For Stochastic Optimization ⭐ 6

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.

C++ Optimization Library

Agents in an environment learning to sense rewards. Neural Networks are trained with Particle Swarm Optimization

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