Version 1
: Received: 28 February 2024 / Approved: 29 February 2024 / Online: 29 February 2024 (12:49:21 CET)
How to cite:
Wang, H.; Alidaee, B.; Sagbansua, L. Efficient Solutions for Large-Scale Max-Cut Problems: A Hybrid Local Search Heuristic Approach and Comparative Analysis with Quantum Annealing. Preprints2024, 2024021698. https://doi.org/10.20944/preprints202402.1698.v1
Wang, H.; Alidaee, B.; Sagbansua, L. Efficient Solutions for Large-Scale Max-Cut Problems: A Hybrid Local Search Heuristic Approach and Comparative Analysis with Quantum Annealing. Preprints 2024, 2024021698. https://doi.org/10.20944/preprints202402.1698.v1
Wang, H.; Alidaee, B.; Sagbansua, L. Efficient Solutions for Large-Scale Max-Cut Problems: A Hybrid Local Search Heuristic Approach and Comparative Analysis with Quantum Annealing. Preprints2024, 2024021698. https://doi.org/10.20944/preprints202402.1698.v1
APA Style
Wang, H., Alidaee, B., & Sagbansua, L. (2024). Efficient Solutions for Large-Scale Max-Cut Problems: A Hybrid Local Search Heuristic Approach and Comparative Analysis with Quantum Annealing. Preprints. https://doi.org/10.20944/preprints202402.1698.v1
Chicago/Turabian Style
Wang, H., Bahram Alidaee and Lutfu Sagbansua. 2024 "Efficient Solutions for Large-Scale Max-Cut Problems: A Hybrid Local Search Heuristic Approach and Comparative Analysis with Quantum Annealing" Preprints. https://doi.org/10.20944/preprints202402.1698.v1
Abstract
In this study, we address the formidable challenge of solving large-scale Max-Cut problems (MCP). We introduce a rapid computational procedure utilizing a hybrid 1-flip/r-flip local search heuristic. This innovative strategy significantly reduces the computational time required for MCP problems while consistently generating solutions of exceptional quality. The paper presents substantial computational insights, showcasing the effectiveness of our approach on very-large-scale Max-Cut instances with varying densities. Our proposed heuristic is rigorously evaluated by comparing its performance against a quantum annealing solver, leveraging a multi-start Tabu Search framework. The results underscore the potency of this unique combination as an efficient and effective solution for large-scale QUBO problems. Notably, our hybrid heuristic consistently delivers high-quality solutions within the stringent CPU time limits of 600 seconds, demonstrating its efficacy across Max-Cut instances ranging from 10,000 to 40,000 variables. This research contributes to advancing the state-of-the-art in large-scale QUBO problem-solving, offering a powerful and time-efficient approach with broad applicability.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
