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#

grey-wolf-optimizer

Here are 30 public repositories matching this topic...

灰狼优化算法(GWO)路径规划/轨迹规划/轨迹优化、多智能体/多无人机航迹规划

  • UpdatedApr 13, 2024
  • MATLAB
swarmlib

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)

  • UpdatedDec 16, 2020
  • Python
zoofs

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.

  • UpdatedJun 20, 2025
  • Python

Using GreyWolfOptimization for feature selection and multi kernel SVM for classification for Malware Hunting on IoT devices

  • UpdatedFeb 14, 2021
  • Python

The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented to perform optim…

  • UpdatedAug 19, 2020
  • MATLAB

Demonstration on how binary grey wolf optimization (BGWO) applied in the feature selection task.

  • UpdatedMar 7, 2021
  • MATLAB

This toolbox offers advanced feature selection tools. Several modifications, variants, enhancements, or improvements of algorithms such as GWO, FPA, SCA, PSO and SSA are provided.

  • UpdatedJan 10, 2021
  • Python

Metaheuristic(Genetic algorithm, Particle swarm optimization, Cuckoo search, Grey wolf optimizer), Reinforcement Learning with Python

  • UpdatedSep 8, 2021
  • Python

A hybrid approach was developed to predict NASA website queries using neural networks and metaheuristic optimization algorithms. The weights of the model was optimized using GWO, PSO, and ICA, harnessing the strengths of these algorithms to achieve remarkable results.

  • UpdatedJul 30, 2022
  • MATLAB

Data Science Project (Australian Electricity Load Dataset Analysis)

  • UpdatedDec 7, 2023
  • Jupyter Notebook

This repositories include the IEEE Congress on Evolutionary Computation Benchmark functions suite (IEEE CEC 2014 2017 2020 2022). You can use the untitled.m to form a figure of the benchmark function.

  • UpdatedJun 20, 2025
  • C++

This project uses Improved Grey Wolf Optimizer (IGWO) and Improved Particle Swarm Optimization (IPSO) for robot path planning with Laser Range Finder (LRF) data reduction in CoppeliaSim (V-REP). Robots autonomously navigate unknown environments and avoid collisions using IGWO/IPSO.

  • UpdatedOct 4, 2024

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