Applying ACO To Large Scale TSP Instances

TL;DR

This paper introduces an optimized ACO methodology that significantly reduces memory and computational costs, enabling the solution of large-scale TSP instances with up to 200,000 cities efficiently on a single CPU.

Contribution

The paper presents a novel ACO approach that minimizes memory use, leverages parallel CPU hardware, and introduces efficiency measures to solve large TSPs more accurately and quickly.

Findings

Achieved up to 1200-fold speedup in solving large TSPs.

Successfully solved TSP instances with up to 200,000 cities.

Enhanced accuracy and efficiency over traditional ACO methods.

Abstract

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.