On the m-Way Graph Partitioning Problem

Abstract

The m-way graph partitioning problem (GPP) is an intractable combinatorial optimization problem with many important applications in the design automation of VLSI circuits and in the mapping problem for distributed computing systems. In this paper, we introduce a technique based on a problem-space genetic algorithm (PSGA) for the GPP to reduce the weighted cut-size while keeping the size of each subset balanced. The proposed PSGA based approach integrates a problem-specific simple and fast heuristic with a genetic algorithm to search a large solution space efficiently and effectively to find the best possible solution in an acceptable CPU time. Experimental study shows that our technique produces better results with respect to both the quality of the solution and the computational time over the previous work. The PSGA is a simple, versatile and a generic optimization technique which can also be applied to other combinatorial optimization problems.