Solving Graph Partitioning Problems with Parallel Metaheuristics

  • Zbigniew KokosińskiEmail author
  • Marcin Bała
Part of the Studies in Computational Intelligence book series (SCI, volume 717)


In this article we describe computer experiments while testing a family of parallel and hybrid metaheuristics against a small set of graph partitioning problems like clustering, partitioning into cliques and coloring. In all cases the search space is composed of vertex partitions satisfying specific problem requirements. The solver application contains two sequential and nine parallel/hybrid algorithms developed on the basis of SA and TS metaheuristics. A number of tests are reported and conclusions concerning metaheuristics’ performance that result from the conducted experiments are derived. The article provides a case study in which partitioning numbers \(\psi _{k}(G)\), \(k \ge 2\), of DIMACS graph coloring instances are evaluated experimentally by means of H-SP metaheuristic which is found to be the most efficient in terms of solution quality.


Simulated annealing Tabu search Parallel metaheuristic Hybrid metaheuristic Graph partitioning problem Graph partitioning number 


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© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Faculty of Electrical and Computer EngineeringCracow University of TechnologyKrakówPoland
  2. 2.Salumanus Sp. z o.o.KrakówPoland

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