firefly-algorithm

Firefly Algorithm Clustering with Python

Machine learning-based Fake News Detection System using Firefly Algorithm feature optimization, MSVM classification, and Django.

Firefly Algorithm on Minimizing Functions (Ackley, Rosenbrock etc.)

Using Firefly Algorithm to find the minimal solution of Rastrigin function and Styblinski-Tang function

This project presents a comparative analysis between the Firefly Optimization Algorithm (FOA) and the classical approach of gradient descent in optimizing a Neural Network.

ANN merged with the firefly algorithm

Nature inspired firefighter assistant by Unmanned Aerial Vehicle (UAV) data

optimize merging vehicles on highway ramps

End to end machine learning pipeline for intrusion detection using CIC IoV dataset. Includes preprocessing feature selection with PSO and Firefly multiple models evaluation and explainability using SHAP and LIME with clear visualizations and comparisons

Firefly algorithm is a bio-inspired metaheuristic algorithm for optimization problems.

An implementation of 3 swarm optimization algorithms: Firefly Algorithm, Harmony Search and Particle Swarm. All algorithms are made to be modular and easily importable to other projects for use.

Fake news detection comparing a baseline SVM against an SVM trained on Binary Firefly Algorithm selected TF-IDF features. 94.83% accuracy on 28.1% fewer features.

A hybrid multi-objective firefly algorithm for big data optimization

This repository contains all materials related to my Master's Thesis titled "Generative Artificial Intelligence and Optimisation Framework for Sustainable Concrete Mixture Design", including the thesis report, presentations, research papers, dataset, and associated code files used for experiments and analysis.

This repo solves TSP with Firefly algorithm.

Firefly Algorithm benchmark TSP

Coursework for CSCI-633 Bio-Inspired Intelligent Systems

Firefly algorithm

Basic Optimization Algorithm Visualizer using Processing.

This project optimizes task allocation in Fog and Cloud Computing environments using multi-objective optimization techniques. It computes and analyzes Pareto fronts using MOCS and MOFA algorithms. The project includes Jupyter notebooks for data preparation, Pareto front calculation, and solution analysis.