MMAS on GPU for Large TSP Instances

Abstract

This paper presents a GPU-based Ant Colony Optimization (ACO) algorithm to solve the large-size Traveling Salesman Problem (TSP). The aim of this study is to provide an effective and efficient GPU-based parallel strategy for the ACO algorithm to solve larger size TSPLIB instances in a reasonable time, preserving its solution quality. Till the date, all GPU-based parallel implementations of the ACO algorithm is only available for TSPLIB instances up to 2392 cities. We have studied the limiting factors of the existing GPU implementations to solve larger instances and designed the parallel model which solves TSPLIB instances up to 33810 cities. Our parallel approach receives speedup up to 60� over its sequential counterpart. In terms of solution quality, our parallel ACO produces the local optimal solution which has error rates in range of 0.52% - 4.97% compared to the optimal solution. � 2019 IEEE.