Parallel GPU implementation of iterated local search for the travelling salesman problem

Authors:
Audrey Delévacq;Pierre Delisle;Michaël Krajecki
Affiliations:
CReSTIC, Université de Reims Champagne-Ardenne, Reims, France;CReSTIC, Université de Reims Champagne-Ardenne, Reims, France;CReSTIC, Université de Reims Champagne-Ardenne, Reims, France
Venue:
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Year:
2012

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Abstract

The purpose of this paper is to propose effective parallelization strategies for the Iterated Local Search (ILS) metaheuristic on Graphics Processing Units (GPU). We consider the decomposition of the 3-opt Local Search procedure on the GPU processing hardware and memory structure. Two resulting algorithms are evaluated and compared on both speedup and solution quality on a state-of-the-art Fermi GPU architecture. We report speedups of up to 6.02 with solution quality similar to the original sequential implementation on instances of the Travelling Salesman Problem ranging from 100 to 3038 cities.