harris-hawks-optimization

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

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

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.

Feature Selection using Metaheuristics Made Easy: Open Source MAFESE Library in Python

Harris Hawks Optimization (HHO) is a nature-inspired metaheuristic algorithm that simulates the cooperative hunting behavior of Harris' hawks. Widely used in engineering, machine learning, and resource allocation, HHO is renowned for its simplicity, versatility, and effectiveness in finding global optima.

This is a collection of Metaheuristics Algorithms written in Julia

Harris Hawks Optimization (HHO) - Python Code

The biggest module developed with complete focus on Feature Selection (FS) using Meta-Heuristic Algorithm / Nature-inspired evolutionary / Swarm-based computing.

The binary version of Harris Hawk Optimization (HHO), called Binary Harris Hawk Optimization (BHHO) is applied for feature selection tasks.

An LSTM model optimized with Harris Hawks Optimization (HHO) for customizable time series forecasting using your own dataset

Multilevel thresholding segmentation method

Proposing a novel approach on using Naives Bayes by using Robust Kernel Density Estimation (RKDE) and optimising the bandwith(h) with Harris Hawks Optimization (HHO)

Efficient Time-series Forecasting using Neural Network and Opposition-based Coral Reefs Optimization

Penerapan Harris Hawks Optimization Pada Capacitated Vehicle Routing Problem

Matlab implementation of the article: A secure data hiding approach based on least-significant-bit and nature-inspired optimization techniques

This repository contains the Harris Hawks Optimization code (matlab M-file) for optimizing the benchmark function.

Metaheuristic optimization algorithms racing live on benchmark functions — 6 optimizers, animated contour plots, user-defined objective functions (safe sympy parsing), and Wilcoxon-based statistical comparison.

Hybrid Grey Wolf–Harris Hawks metaheuristic optimization algorithm (undergraduate graduation project) — validated on CEC2017 benchmarks and applied to real-world smart energy management scheduling using NASA POWER & EPİAS data.

Reproducible simulations for ECB-HD-CR: energy-credit scheduling and routing in energy-harvesting wireless sensor networks.