Distributed Evolutionary k-way Node Separators

TL;DR

This paper introduces a distributed evolutionary algorithm for high-quality k-way node separators in large graphs, combining local search, multilevel methods, and scalable communication to outperform existing algorithms.

Contribution

It presents a novel distributed evolutionary approach with new local search algorithms and modifications to multilevel coarsening for improved k-way node separator solutions.

Findings

Achieves best results on 94% of benchmark instances

Combines local search, multilevel methods, and scalable communication effectively

Outperforms competing algorithms in quality and speed

Abstract

Computing high quality node separators in large graphs is necessary for a variety of applications, ranging from divide-and-conquer algorithms to VLSI design. In this work, we present a novel distributed evolutionary algorithm tackling the k-way node separator problem. A key component of our contribution includes new k-way local search algorithms based on maximum flows. We combine our local search with a multilevel approach to compute an initial population for our evolutionary algorithm, and further show how to modify the coarsening stage of our multilevel algorithm to create effective combine and mutation operations. Lastly, we combine these techniques with a scalable communication protocol, producing a system that is able to compute high quality solutions in a short amount of time. Our experiments against competing algorithms show that our advanced evolutionary algorithm computes the best result on 94% of the chosen benchmark instances.