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

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)

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

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.

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.

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.

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

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…

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

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.

Nature Inspired Optimization Algorithms

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

🕸 CNN + 🛍 BoVW + 💼 BoCF + 🐺 Grey Wolf Optimization & Comparision ⚖

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.

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.

Julia implementations of various animal-inspired optimizers

Data Science Project (Australian Electricity Load Dataset Analysis)

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.