Parallel GPU Implementation of Iterated Local Search for the Travelling Salesman Problem

Audrey Delevacq, Pierre Delisle, Michael Krajecki
CReSTIC, Universite de Reims Champagne-Ardenne, Reims, France
Learning and Intelligent OptimizatioN conference (LION), 2012


   title={Parallel GPU Implementation of Iterated Local Search for the Travelling Salesman Problem},

   author={Del{‘e}vacq, A. and Delisle, P. and Krajecki, M.},



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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.
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