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The improved local cost simulation algorithms based on coalition structure generation for solving distributed constraint optimization problems

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Abstract

Distributed constraint optimization problems (DCOPs) are a fundamental framework for modeling multi-agent systems (MAS) where agents coordinate their decision-making to optimize a global objective. However, existing incomplete local search algorithms for solving DCOPs face a huge challenge of falling into the local optimum since the information and controls are distributed among multiple autonomous agents. Considering this, two improved local cost simulation algorithms based on coalition structure generation (CSG) named LCS-CSG-F and LCS-CSG-C that execute CSG in fixed and consecutive rounds, respectively, are proposed in this paper to expand the search for solution space. CSG involves partitioning a set of agents into disjoint exhaustive coalitions according to the neighbor relationship between agents, where agents of the original DCOPs are partitioned into coalitions to relax constraints between agents. To verify the effectiveness of the CSG strategy, two competing schemes that execute neighbor ignoring in fixed rounds, named asymmetric single neighbor ignoring (ASI-F) and asymmetric multiple neighbor ignoring (AMI-F), are designed since the CSG strategy symmetrically ignores multiple neighbors in different coalitions simultaneously. From statistical perspective, the Friedman test reveals significant differences between the LCS-CSG algorithm and other algorithms. Extensive experimental results indicate that the proposed CSG-based algorithms significantly outperform the state-of-the-art DCOPs incomplete algorithms in various benchmarks.

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The code is available in the Supplementary File.

References

  1. Ferber J (1999) Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence, vol 1. Addison-Wesley Longman Publishing Co., Inc, USA

  2. Leite AR, Enembreck F, Barthès J-PA (2014) Distributed constraint optimization problems: review and perspectives. Expert Syst Appl 41:5139–5157

    Article  Google Scholar 

  3. Sato DMV, Borges AP, Márton P, Scalabrin EE (2015) I-dcop: train classification based on an iterative process using distributed constraint optimization. Proc Comput Sci 51(1):2297–2306

    Article  Google Scholar 

  4. Zivan R, Parash T, Naveh Y (2015). Applying max-sum to asymmetric distributed constraint optimization. In: Proceedings of the 24th International Conference on Artificial Intelligence. IJCAI’15, 432–438

  5. Sultanik E.A, Modi P.J, Regli W.C (2007). On modeling multiagent task scheduling as a distributed constraint optimization problem. In: Proceedings of the 20th International Joint Conference on Artifical Intelligence. IJCAI’07, 1531–1536. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA

  6. Acevedo JJ, Arrue BC, Maza I, Ollero A (2013) Cooperative large area surveillance with a team of aerial mobile robots for long endurance missions. J Int Robot Syst 70:329–345

    Article  Google Scholar 

  7. Präntare F, Heintz F (2020) An anytime algorithm for optimal simultaneous coalition structure generation and assignment. Auton Agents Multi-Agent Syst 34(1):29

    Article  Google Scholar 

  8. Contreras JP, Bosch P, Varas M, Basso F (2020) A new genetic algorithm encoding for coalition structure generation problems. Math Prob Eng 2020(1):1203248

    MathSciNet  Google Scholar 

  9. Farinelli A, Rogers A, Petcu A, Jennings N.R (2008). Decentralised coordination of low-power embedded devices using the max-sum algorithm. In: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2. AAMAS ’08, 639–646. International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC

  10. Rogers A, Farinelli A, Stranders R, Jennings NR (2009) Bounded approximate decentralised coordination via the max-sum algorithm. Artif Intell 175:730–759

    Article  MathSciNet  Google Scholar 

  11. Zivan R, Parash T, Cohen L, Peled H, Okamoto S (2017) Balancing exploration and exploitation in incomplete min/max-sum inference for distributed constraint optimization. Auton Agents Multi-Agent Syst 31:1165–1207

    Article  Google Scholar 

  12. Cohen L, Zivan R (2017). Max-sum revisited: The real power of damping. In: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems. AAMAS ’17, 1505–1507. International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC

  13. Ottens B, Dimitrakakis C, Faltings B (2017) Duct: An upper confidence bound approach to distributed constraint optimisation problems *. ACM Trans Int Syst 8(5):69–16927

    Google Scholar 

  14. Nguyen D.T, Yeoh W, Lau H.C (2013). Distributed gibbs: a memory-bounded sampling-based dcop algorithm. In: Proceedings of the 2013 International Conference on Autonomous Agents and Multi-Agent Systems. AAMAS ’13, 167–174. International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC

  15. Nguyen DT, Yeoh WGS, Lau HC, Zivan R (2019) Distributed gibbs: a linear-space sampling-based DCOP algorithm. J Artif Intell Res 64:705–748

    Article  MathSciNet  Google Scholar 

  16. Zhang W, Wang G, Xing Z, Wittenburg L (2005) Distributed stochastic search and distributed breakout: properties, comparison and applications to constraint optimization problems in sensor networks. Artif Int 161(1):55–87. https://doi.org/10.1016/j.artint.2004.10.004

    Article  MathSciNet  Google Scholar 

  17. Maheswaran R.T, Pearce J.P, Tambe M (2004). Distributed algorithms for dcop: A graphical-game-based approach. In: Parallel and Distributed Computing Systems (ISCA), 432–439 . https://api.semanticscholar.org/CorpusID:5531786

  18. Maheswaran RT, Pearce JP, Tambe M (2006). In: Scerri P, Vincent R, Mailler R (eds) A Family of Graphical-Game-Based Algorithms for Distributed Constraint Optimization Problems. Springer, Boston, MA, USA

    Chapter  Google Scholar 

  19. Arshad M, Silaghi MC (2004). Distributed simulated annealing. Distributed constraint problem solving and reasoning in multi-agent systems 112

  20. Yu Z, Chen Z, He J, Deng Y (2017). A partial decision scheme for local search algorithms for distributed constraint optimization problems. In: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems. AAMAS ’17, 187–194. International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC

  21. Okamoto S, Zivan R, Nahon A (2016). Distributed breakout: Beyond satisfaction. IJCAI International Joint Conference on Artificial Intelligence 2016-January, 447–453 . 25th International Joint Conference on Artificial Intelligence, IJCAI 2016 ; Conference date: 09-07-2016 Through 15-07-2016

  22. Chen Z, Wu T, Deng Y, Zhang C (2018). An ant-based algorithm to solve distributed constraint optimization problems. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence. AAAI’18/IAAI’18/EAAI’18

  23. Chen Z, Liu L, He J, Yu Z (2020) A genetic algorithm based framework for local search algorithms for distributed constraint optimization problems. Auton Agents Multi-Agent Syst 34:1–31

    Google Scholar 

  24. Li F, He J, Zhou M, Fang B (2022) Vlss: a local search algorithm for distributed constraint optimization problems. Int J Pattern Recognit Artif Int 36(03):2259006

    Article  Google Scholar 

  25. Shi M, Xiao S, Feng X (2023) An adaptive ant colony algorithm based on local information entropy to solve distributed constraint optimization problems. Int J Pattern Recognit Artif Int 37(09):2359013

    Article  Google Scholar 

  26. Shi M, Liang F, Chen Y, He Y (2023) A local cost simulation-based algorithm to solve distributed constraint optimization problems. Peer J Comput Sci 9:e1296

    Article  Google Scholar 

  27. Ueda S, Iwasaki A, Yokoo M, Calin Silaghi M, Hirayama K, Matsui T (2010). Coalition structure generation based on distributed constraint optimization. In: AAAI-10 / IAAI-10 - Proceedings of the 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference. Proceedings of the National Conference on Artificial Intelligence, 197–203. AI Access Foundation, United States

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Funding

This work is supported by the Youth Project of Science and Technology Research Program of Chongqing Education Commission of China (NO. KJQN202401101), and the Postgraduate Innovation Project of Chongqing University of Technology (NO.gzlcx20232058).

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Author notes

  1. Guoyan Jia and Makoto Yokoo have contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science and Engineering, Chongqing University of Technology, No.69 Hongguang, Banan, Chongqing, 400054, China

    Meifeng Shi & Guoyan Jia

  2. Faculty of Information Science and Electrical Engineering, Kyushu University, 744 Motooka, Nishi-Ward, Fukuoka, 8190395, Japan

    Meifeng Shi & Makoto Yokoo

Authors
  1. Meifeng Shi
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  2. Guoyan Jia
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  3. Makoto Yokoo
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Contributions

Meifeng Shi conceived and designed the experiments, performed the experiments, analyzed the data, prepared Figures 1-5, authored and reviewed drafts of the article and approved the final draft. Guoyan Jia performed the experiments, prepared other figures and tables, authored drafts of the article and approved the final draft. Makoto Yokoo analyzed the data, reviewed drafts of the article and approved the final draft.

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Correspondence to Meifeng Shi.

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Shi, M., Jia, G. & Yokoo, M. The improved local cost simulation algorithms based on coalition structure generation for solving distributed constraint optimization problems. J Supercomput 81, 132 (2025). https://doi.org/10.1007/s11227-024-06644-2

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