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)

A Vehicle Routing Problem solver

OptaPy is an AI constraint solver for Python to optimize planning and scheduling problems.

A python library with implementations of 15 classical heuristics for the capacitated vehicle routing problem.

A collection of NVIDIA cuOpt samples and other resources

[NeurIPS 2023] DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization

[NeurIPS 2024] ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution

Improve Warehouse Productivity using Order Batching

A Python wrapper for the Hybrid Genetic Search algorithm for Capacitated Vehicle Routing Problems (HGS-CVRP)

Library for working with TSPLIB files.

A Kotlin implementation of the TSP problem, with TornadoFX Visualization

Julia package for simple traveling salesman problem heuristics

A dependency free library of standardized optimization test functions written in pure Python.

Genetic algorithm for Traveling salesman problem

A simple, bare bones, implementation of simulated annealing optimization algorithm.

This code is to solve traveling salesman problem by using simulated annealing meta heuristic.

Simple Traveling Salesman Problem implementation in Julia

A Greedy Randomized Adaptive Search Procedure (GRASP) for the Traveling Salesman Problem (TSP)

Genetic Algorithm + Google Maps API to solve the Traveling Salesman (NP-hard) Problem. Plan your travel over 100+ places with ease.

metaheuristics for traveling salesman problem (TSP)