# Applying ACO To Large Scale TSP Instances

arXiv: https://arxiv.org/abs/1709.03187 · HTML: https://mlsanity.com/paper/1709.03187/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2017.

## Built on by (most cited first, 2 of 2)

- [Improving Ant Colony Optimization Efficiency for Solving Large TSP Instances](https://mlsanity.com/paper/2203.02228/) (2022)
- [Beyond Static Priors: Dynamic Neural Guidance for Large-Scale Ant Colony Optimization](https://mlsanity.com/paper/2606.04039/) (2026)

## Abstract (arXiv)

Ant Colony Optimisation (ACO) is a well known metaheuristic that has proven successful at solving Travelling Salesman Problems (TSP). However, ACO suffers from two issues; the first is that the technique has significant memory requirements for storing pheromone levels on edges between cities and second, the iterative probabilistic nature of choosing which city to visit next at every step is computationally expensive. This restricts ACO from solving larger TSP instances. This paper will present a methodology for deploying ACO on larger TSP instances by removing the high memory requirements, exploiting parallel CPU hardware and introducing a significant efficiency saving measure. The approach results in greater accuracy and speed. This enables the proposed ACO approach to tackle TSP instances of up to 200K cities within reasonable timescales using a single CPU. Speedups of as much as 1200 fold are achieved by the technique.