Application of Parallel and Hybrid Metaheuristics for Graph Partitioning Problem
In this paper parallel and hybrid metaheuristics for graph partitioning are compared taking into account their efficiency in terms of a cost function and computation time. Seventeen methods developed on the basis of evolutionary algorithm, simulated annealing and tabu search are implemented and tested against graph instances computed on the basis of queen graphs from DIMACS repository and a class of random R–MAT graphs. These graphs are supposed to model a class of digital circuits being subject of decomposition into a given number of modules. In partitioning process several additional constraints have to be satisfied in order to enable composition of original circuits from subcircuits by means of VLSI/FPGA modules.
KeywordsGraph partitioning Circuit partitioning Parallel metaheuristics Hybrid metaheuristics Approximate algorithms DIMACS graphs R–MAT graphs
This work was supported by the research grant No. E–3/611/2017/DS from Cracow University Technology.
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