The GPU-based Parallel Ant Colony System

TL;DR

This paper introduces three novel GPU-based parallel versions of the Ant Colony System (ACS), achieving significant speedups and improved solution quality for the Traveling Salesman Problem compared to sequential implementations.

Contribution

The paper presents the first GPU parallel implementations of ACS, including a novel selective pheromone memory, demonstrating substantial performance and solution quality improvements.

Findings

Speedup up to 24.29x over sequential ACS.

Parallel ACS with selective pheromone memory yields better solutions.

Effective GPU implementation for large TSP instances.

Abstract

The Ant Colony System (ACS) is, next to Ant Colony Optimization (ACO) and the MAX-MIN Ant System (MMAS), one of the most efficient metaheuristic algorithms inspired by the behavior of ants. In this article we present three novel parallel versions of the ACS for the graphics processing units (GPUs). To the best of our knowledge, this is the first such work on the ACS which shares many key elements of the ACO and the MMAS, but differences in the process of building solutions and updating the pheromone trails make obtaining an efficient parallel version for the GPUs a difficult task. The proposed parallel versions of the ACS differ mainly in their implementations of the pheromone memory. The first two use the standard pheromone matrix, and the third uses a novel selective pheromone memory. Computational experiments conducted on several Travelling Salesman Problem (TSP) instances of sizes ranging from 198 to 2392 cities showed that the parallel ACS on Nvidia Kepler GK104 GPU (1536 CUDA cores) is able to obtain a speedup up to 24.29x vs the sequential ACS running on a single core of Intel Xeon E5-2670 CPU. The parallel ACS with the selective pheromone memory achieved speedups up to 16.85x, but in most cases the obtained solutions were of significantly better quality than for the sequential ACS.