There are 2 repositories under cuckoo-search topic.
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)
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.
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.
Nature Inspired Optimization Algorithms
Implemented fully documented Cuckoo Search Optimization algorithm via Levy Flights (basic model) using Python programming language
Metaheuristic(Genetic algorithm, Particle swarm optimization, Cuckoo search, Grey wolf optimizer), Reinforcement Learning with Python
Neuromorphic Project : Pattern Classification using Cuckoo Search Algorithm and Levy Flight Model
different algorithms to find population close to the optimal fitness, including genetic algorithm, differential evolution algorithm, PSO, firefly algorithm, cuckoo search algorithm and whale optimization algorithm in C++.
Expandable Cuckoo hash map/set implementation in Go (Docs WIP)
数据挖掘课程项目
Implementation of IIR and FIR digial filter optimization using ABC, Cuckoo Search and Particle Search Algorithm
Solutions for Labs of Nature Inspired Computing course offered at Innopolis University
This repository contains 4 metaheuristic search algorithm for optimization
The Repository dedicated to code and more for Swarm Intelligence furthermore.
Repository of Swarm Intelligence Course (CSH4X3) Assignment
Solving different problems, using metaheuristic algorithms
R implementation of "Engineering Optimisation by Cuckoo Search"
This project aimed to implement three well-known meta-heuristic algorithms: cuckoo search (CS), bat algorithm (BA), and flower pollination algorithm (FPA). We found that three algorithms could have a promising performance generally. It might need more runs to be converged when training BA. The time cost of BA was the highest while the differences of time cost among three algorithms were not so large, which might not matter when the number of training runs was not large. We also tuned λ in CS and FPA with the findings of the weakness of larger λ values. A demo video of training process is available on YouTube: https://youtu.be/hlKvODBUyeI.
Trabajo Final de la asignatura del Master Universitario en Inteligencia Artificial de la Universidad Politécnica de Madrid