Applying ACO To Large Scale TSP Instances
This addresses scalability issues for researchers and practitioners using ACO in optimization, though it is incremental as it builds on existing ACO methods.
The paper tackled the problem of Ant Colony Optimization (ACO) being restricted to small Travelling Salesman Problem (TSP) instances due to high memory and computational costs, and achieved a method that solves instances up to 200K cities with speedups of up to 1200-fold.
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.