Think Locally, Act Globally: Perfectly Balanced Graph Partitioning

TL;DR

This paper introduces a novel local improvement scheme for perfectly balanced graph partitioning, combining negative cycle detection with evolutionary algorithms to achieve state-of-the-art results efficiently.

Contribution

It presents a new local search method that maintains perfect balance using negative cycle detection and integrates it into a parallel evolutionary framework.

Findings

Achieves or improves most of the best known balanced partitioning results.

Fast algorithm with competitive performance.

Effective combination of local search and evolutionary strategies.

Abstract

We present a novel local improvement scheme for the perfectly balanced graph partitioning problem. This scheme encodes local searches that are not restricted to a balance constraint into a model allowing us to find combinations of these searches maintaining balance by applying a negative cycle detection algorithm. We combine this technique with an algorithm to balance unbalanced solutions and integrate it into a parallel multi-level evolutionary algorithm, KaFFPaE, to tackle the problem. Overall, we obtain a system that is fast on the one hand and on the other hand is able to improve or reproduce most of the best known perfectly balanced partitioning results ever reported in the literature.