Multilevel hybrid graph partitioning algorithm

Abstract

Balanced graph partition is a type of graph partitioning problem that divides the given graph into components, such that the components are of about the same size and there are few connections between the components. The existing approaches partition the graph initially in a random manner which has a very high impact on determining the final quality of the solution. Recently, Multilevel Partitioning methods are proven to be faster among other approaches. This paper proposes a multilevel hybrid algorithm for balanced graph partitioning. Here graph is initially partitioned using Balanced Big method in order to improve the initial solution quality. Further, the quality of the obtained solution is improved using local search refinement procedure. The experimental results indicate that the relatively good initial partitions, when subjected to local search techniques like tabu search and hill climbing, results in better solutions. The experimental results also indicate that for the proposed approach, when the number of partitions increase (are high), the quality of the solution is better than the currently available solutions reported in the existing approaches.