A Novel Local Search Algorithm for the Vertex Bisection Minimization Problem
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 8956-8965.
https://doi.org/10.24963/ijcai.2025/996
Keywords
- Search: S: Heuristic search
- Search: S: Local search
Abstract
The vertex bisection minimization problem (VBMP) is a fundamental graph partitioning problem with numerous real-world applications. In this study, we propose a (k, l, S)-cluster guided local search algorithm to address this challenge.
First, we propose a novel (k,l,S)-cluster enumeration procedure, which is based on two key concepts: the (k, l, S)-cluster and the local cluster core. The (k, l, S)-cluster limits both the connectivity and distinct boundaries of a given vertex set, and the local cluster core represents the most cohesive substructure within a (k, l, S)-cluster. Building up on the above (k, l, S)-cluster enumeration procedure, we present a novel (k, l, S)-cluster guided perturbation mechanism designed to escape from local optima.
Next, we propose a two-manner local search procedure that employs two distinct search models to explore the neighboring search space efficiently. Experimental results demonstrate that the proposed algorithm performs best on nearly all instances.
@inproceedings{ijcai2025p996,
title = {A Novel Local Search Algorithm for the Vertex Bisection Minimization Problem},
author = {Sun, Rui and Wang, Xinyu and Wang, Yiyuan and Li, Jiangnan and Zhou, Yi},
booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-25}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {James Kwok},
pages = {8956--8965},
year = {2025},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2025/996},
url = {https://doi.org/10.24963/ijcai.2025/996},
}
TY - CPAPER TI - A Novel Local Search Algorithm for the Vertex Bisection Minimization Problem AU - Sun, Rui AU - Wang, Xinyu AU - Wang, Yiyuan AU - Li, Jiangnan AU - Zhou, Yi T2 - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2025 SP - 8956 EP - 8965 DO - 10.24963/ijcai.2025/996 UR - https://doi.org/10.24963/ijcai.2025/996 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: