Optimising Graph Partitions Using Parallel Evolution

Abstract

The graph partitioning problem consists of dividing the vertices of a graph into a set of balanced parts, such that the number of edges connecting vertices in different parts is minimised. Although different algorithms to solve this problem have been proposed in complex graphs, it is unknown how good the partitions are since the problem is, in general, NP-complete. In this paper we present a new parallel evolutionary algorithm for graph partitioning where different heuristics, such Simulated Annealing, Tabu Search, and some Selection Mechanisms are mixed. The efficiency of the new algorithm is compared with other previously proposed algorithms with promising results. © Springer-Verlag 2004.