Version 1
: Received: 15 January 2024 / Approved: 15 January 2024 / Online: 16 January 2024 (08:47:39 CET)
How to cite:
Liao, X.; Hoang, K. A Class of Local Search Based Anytime Algorithms for Continuous Distributed Constraint Optimization Problems. Preprints2024, 2024011158. https://doi.org/10.20944/preprints202401.1158.v1
Liao, X.; Hoang, K. A Class of Local Search Based Anytime Algorithms for Continuous Distributed Constraint Optimization Problems. Preprints 2024, 2024011158. https://doi.org/10.20944/preprints202401.1158.v1
Liao, X.; Hoang, K. A Class of Local Search Based Anytime Algorithms for Continuous Distributed Constraint Optimization Problems. Preprints2024, 2024011158. https://doi.org/10.20944/preprints202401.1158.v1
APA Style
Liao, X., & Hoang, K. (2024). A Class of Local Search Based Anytime Algorithms for Continuous Distributed Constraint Optimization Problems. Preprints. https://doi.org/10.20944/preprints202401.1158.v1
Chicago/Turabian Style
Liao, X. and Khoi Hoang. 2024 "A Class of Local Search Based Anytime Algorithms for Continuous Distributed Constraint Optimization Problems" Preprints. https://doi.org/10.20944/preprints202401.1158.v1
Abstract
Continuous Distributed Constraint Optimization Problem (C-DCOPs) are a powerful framework to model problems with continuous variables in multi-agent systems. Previous works about C-DCOP algorithms mainly use pseudo-trees to guarantee the anytime property or are without anytime property guarantees. However, there is a risk of privacy leakage in the pseudo-tree due to utility passing between agents. Therefore, based on the basic constraint graph instead of the pseudo-tree, we (i) extend the Maximum Gain Message (MGM) algorithm by combining the local search strategy to solve C-DCOPs, named Continuous MGM (C-MGM), and it’s able to guarantee the monotonicity of the solution quality; (ii) propose a Parallel C-MGM (C-PMGM) algorithm to improve the solution quality through parallel random search; and (iii) introduce the differential search into C-PMGM to design a Parallel Differential Search C-MGM (C-PDSM) algorithm, which constructs a heuristic method to speed up convergence and improve solution quality. Compared to other anytime C-DCOP algorithms using pseudo-trees, the proposed three algorithms can exhibit better performance in avoiding privacy violations. We theoretically prove that the proposed algorithms are the anytime algorithms and empirically demonstrate that our algorithms outperform the state-of-the-art C-DCOP algorithms.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
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.
