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