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A GPU-based genetic algorithm for the p-median problem

Abstract

The p-median problem is a well-known NP-hard problem. Many heuristics have been proposed in the literature for this problem. In this paper, we exploit a GPGPU parallel computing platform to present a new genetic algorithm implemented in CUDA and based on a pseudo-Boolean formulation of the p-median problem. We have tested the effectiveness of our algorithm using a Tesla K40 (2880 CUDA cores) on 290 different benchmark instances obtained from OR-Library, discrete location problems benchmark library, and benchmarks introduced in recent publications. The algorithm succeeded in finding optimal solutions for all instances except for two OR-library instances, namely pmed 30 and pmed 40, where better than 99.9% approximations were obtained.

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Notes

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    We shall use distance and cost interchangeably.

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Correspondence to Bader F. AlBdaiwi.

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AlBdaiwi, B.F., AboElFotoh, H.M.F. A GPU-based genetic algorithm for the p-median problem. J Supercomput 73, 4221–4244 (2017). https://doi.org/10.1007/s11227-017-2006-x

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Keywords

  • P-median problem
  • NP-hard
  • GPGPU
  • CUDA
  • Pseudo-Boolean formulation
  • Genetic algorithms
  • Heuristics