This repository contains the implementation of image contrast enhancement techniques using a hybrid approach that integrates Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Simulated Annealing (SA). The algorithms have been designed to improve the visual quality of images by enhancing their contrast.

Agent based modelling on example of ant colony optimization

Python Implementation of the ACS (Ant Colony System) algorithm. ACS is a metaheuristic algorithm inspired by the foraging behavior of ants.

Ant Colony Optimization of Travelling Salseman Problem

Conducted research in the fusion of machine learning models to improve stock market index prediction accuracy. Evaluated individual models (LSTM, RF, LR, GRU) and compared their performance to fusion prediction models (RF-LSTM, RF-LR, RFGRU).

Continuous Assessment for ECM3412 - Nature-Inspired Computation, set by Prof. Ayah Helal (Year 3, Semester 1). Implements the ant colony optimisation algorithm to address the travelling salesperson problem for two given networks.

Solves the TSP using ACO - Language: Python

Data Science analysis of mock supermarket data

Python based ant colony optimisation algorithm, that tackles the travelling salesman problem. Includes a graph based solution, fitness function, parameter testing and results writeup.

Python solutions for Introduction to AI laboratory exercises.

Simple evolutionary computation for the course Data Design Nature Inspired Computing

Swarmpy_tsp is a Python Library to easily build customizable Ant Colony Optimization pipelines for solving Traveling Salesman Problems

An implementation of ant colony optimisation and various experiments.

Research about applying reinforcement learning to realitic problems. Collects data then figure out how good reinforcement learning is.

Using Ant-Colony Algorithms to solve Travelling salesman problem (TSP).

Our solution to AI Lab Spring 2022-23 at Indian Institute of Technology, Dharwad