IMPart: Integration of Memetic Operations into Multi-Level Framework for Large-k-Way Hypergraph Partitioning

2026-06-16

AI Summary

Existing hypergraph partitioning methods struggle to balance solution quality and computational efficiency in large-k scenarios, particularly due to the prohibitive overhead of traditional memetic algorithms that repeatedly invoke independent multilevel partitioners. This work proposes a novel approach that deeply integrates recombination and mutation operators from memetic computing into the uncoarsening phase of a single multilevel partitioning framework, establishing a cross-granularity cooperative local search mechanism that eliminates redundant partitioner invocations. Evaluated on standard benchmarks, the proposed method significantly outperforms state-of-the-art techniques by more effectively escaping local optima while simultaneously achieving higher-quality partitions for large k without sacrificing efficiency.

Abstract

The problem of k-way hypergraph partitioning is fundamental with significant applications in various fields, including VLSI design and scientific computing. State-of-the-art hypergraph partitioners commonly employ a multi-level framework encompassing coarsening, initial partitioning, uncoarsening, and refinement phases. However, many existing methods do not scale well to problems requiring a large number of partitions (i.e., large k). In pursuit of exceptionally high solution quality, existing memetic approaches often execute their two key operations, recombination and mutation, by invoking separate, standalone multi-level partitioners. This design choice, however, renders them significantly more time-consuming than standard multi-level partitioners. To make such memetic approaches more practical, we propose an advanced memetic framework, IMPart, which introduces novel recombination and mutation operators and integrates them directly into the uncoarsening phase of a single multi-level framework. This transforms the local searches of different granularities in the traditional multi-level framework into a sophisticated, collaborative search. Experimental results on multiple standard benchmarks demonstrate our framework more effectively escapes local optima and explores the global solution space for higher-quality solutions, substantially outperforming all existing hypergraph partitioners for large-$k$-way hypergraph partitioning. Our framework highlights a new paradigm for the development of advanced hypergraph partitioners.

Problem

• hypergraph partitioning
• large-k-way
• memetic algorithm
• scalability
• multi-level framework

Innovation

• memetic algorithm
• hypergraph partitioning
• multi-level framework
• large-k partitioning
• recombination and mutation operators