tsplib

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)

Library for working with TSPLIB files.

A multi-algorithm solver for Travelling Salesman Problem (TSP) focused on automating testing and comparing performance of various algorithms.

This project implements two nature-inspired optimization algorithms: Moth Flame Optimization (MFO) and Honey Badger Optimization (HBO). Both algorithms are designed to solve complex optimization problems by mimicking behaviors observed in nature. also it includes a path finding algorithm, A-star

Python framework to benchmark TSP constructive heuristics on 21 TSPLIB instances (EUC_2D, ATT). Compares 3 novel insertion heuristics (centroid, eccentricity, hybrid) against Nearest Neighbor, Farthest Insertion and Greedy Edge. Generates heatmaps, boxplots and Pareto frontier analysis.

Exact and metaheuristic TSP solvers in Python — Held-Karp, branch and bound, simulated annealing and ant colony optimization — benchmarked against published TSPLIB optima.

Frame selection and spatial partitioning for min-max multi-vehicle routing on grid-structured fields: code, instances and measurements

Genetic algorithm for the Travelling Salesman Problem in pure Python, tested on TSPLIB instances

Qt Application to solve the TSP problem using TSPLIB instances and applied in Google Maps, through hybridization of GRASP and VNS metaheuristics

Python solvers for the Traveling Salesman Problem using two metaheuristics: a Genetic Algorithm and Ant Colony Optimization. Supports TSPLIB datasets with fully configurable parameters.

Comparative metaheuristics for the Sequential Ordering Problem (SOP): Genetic Algorithm vs Particle Swarm Optimization on TSPLIB-SOP.

Hybrid Bees Algorithm with adaptive neighborhood control and 2-opt / lightweight 3-opt-style local search for the Traveling Salesman Problem, compared with BA, ACO, GA, and PSO on six TSPLIB instances