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Variations on memetic algorithms for graph coloring problems

Abstract

Graph vertex coloring with a given number of colors is a well-known and much-studied NP-complete problem. The most effective methods to solve this problem are proved to be hybrid algorithms such as memetic algorithms or quantum annealing. Those hybrid algorithms use a powerful local search inside a population-based algorithm. This paper presents a new memetic algorithm based on one of the most effective algorithms: the hybrid evolutionary algorithm (HEA) from Galinier and Hao (J Comb Optim 3(4): 379–397, 1999). The proposed algorithm, denoted HEAD—for HEA in Duet—works with a population of only two individuals. Moreover, a new way of managing diversity is brought by HEAD. These two main differences greatly improve the results, both in terms of solution quality and computational time. HEAD has produced several good results for the popular DIMACS benchmark graphs, such as 222-colorings for \({<}{} \texttt {dsjc1000.9}{>}\), 81-colorings for \({<}{} \texttt {flat1000\_76\_0}{>}\) and even 47-colorings for \({<}{} \texttt {dsjc500.5}{>}\) and 82-colorings for \({<}{} \texttt {dsjc1000.5}{>}\).

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Notes

  1. 1.

    ftp://dimacs.rutgers.edu/pub/challenge/graph/benchmarks/color/.

  2. 2.

    http://mat.gsia.cmu.edu/COLOR02/.

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Correspondence to Laurent Moalic.

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Moalic, L., Gondran, A. Variations on memetic algorithms for graph coloring problems. J Heuristics 24, 1–24 (2018). https://doi.org/10.1007/s10732-017-9354-9

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Keywords

  • Combinatorial optimization
  • Metaheuristics
  • Coloring
  • Graph
  • Evolutionary