Soft Computing

, Volume 22, Issue 3, pp 845–859 | Cite as

Heuristic routing algorithm toward scalable distributed generalized assignment problem

  • Yang XuEmail author
  • Xiaofeng Wang
  • Tingting Sun
Methodologies and Application


Distributed generalized assignment problem (D-GAP) is very popular in scalable multi-agent systems. However, existing algorithms are either not effective or efficient in large-scale or highly dynamic domains owing to limited communications and computational resources. In this paper, we present a novel approach named intelligent routing algorithm (IRA) to address this issue. In IRA, in order to reduce communication load, a decentralized model for agents is proposed to jointly search for optimized solutions. Moreover, due to the complexity of distributed generalized assignment problem (D-GAP) in a massive multi-agent system where agents cannot perform optimal search based on their local views, we propose a heuristic algorithm that can find an approximate optimized solution. By inferring knowledge from their previous communicated searches, agents are able to predict how to deploy future similar searches more efficiently. If an agent can solve some parts of D-GAP well, similar searches will be sent to it. By taking advantage of the accumulation effect to agents’ local knowledge, agents can independently make simple decisions with highly reliable performance and limited communication overheads. The simulation and the experimental results demonstrate the feasibility and efficiency of our algorithm.


Multi-agent system D-GAP Heuristic routing algorithm 



This study was funded by NSFC 61370151 and 61202211, National Science and Technology Major Project of China 2015ZX03003012, Central University Basic Research Funds Foundation of China ZYGX2014J055 and the Science and Technology on Electronic Information Control Laboratory Project.

Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflict of interest

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. Amato C, Bernstein DS, Zilberstein S (2012) Optimizing memory-bounded controllers for decentralized POMDPs. In: arXiv preprint arXiv:1206.5258
  2. Bloembergen D, Tuyls K, Hennes D et al (2015) Evolutionary dynamics of multi-agent learning: a survey. J Artif Intell Res 659–697Google Scholar
  3. Brown KN (2015) A general framework for reordering agents asynchronously in distributed CSP. In: Proceedings of the 21st International Conference on Principles and Practice of Constraint Programming 2015, vol 9255. SpringerGoogle Scholar
  4. Dunin-Keplicz B, Verbrugge R (2011) Teamwork in multi-agent systems: a formal approach. WileyGoogle Scholar
  5. Ferreira P, Boffo F, Bazzan A (2008) Using swarm-gap for distributed task allocation in complex scenarios. In: Massively multi-agent technology. Springer, Heidelberg, pp 107–121Google Scholar
  6. Gaston M, desJardins M (2005) Social network structures and their impact on multi-agent system dynamics. In: Proceedings of the 18th International Florida Artificial Intelligence Research Society ConferenceGoogle Scholar
  7. Kim Y, Lesser V (2014) A communication-constrained DCOP algorithm that combines features of ADOPT and action-GDL. In: Twenty-Eighth AAAI Conference on Artificial IntelligenceGoogle Scholar
  8. Korsah GA, Stentz A, Dias MB (2013) A comprehensive taxonomy for multi-robot task allocation. Int J Robot Res 32(12):1495–1512Google Scholar
  9. Lesser V, Corkill D (2014) Challenges for multi-agent coordination theory based on empirical observations. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-Agent Systems 1157–1160Google Scholar
  10. Modi PJ, Shen W et al. (2002) Distributed constraint optimization and its application. Technical report ISI-TR-509, University of Southern California/Information Sciences InstituteGoogle Scholar
  11. Modi PJ, Shen WM, Tambe M et al (2003) An asynchronous complete method for distributed constraint optimization. AAMAS 3:161–168Google Scholar
  12. Nourjou R, Szekely P, Hatayama M et al (2014) Data model of the strategic action planning and scheduling problem in a disaster response team. J Disaster Res 9(3):381–399CrossRefGoogle Scholar
  13. Oh KK, Park MC, Ahn HS (2015) A survey of multi-agent formation control. Automatica 53:424–440MathSciNetCrossRefzbMATHGoogle Scholar
  14. Patriksson M (2015) The traffic assignment problem: models and methods. Dover Publication, Inc., MineolaGoogle Scholar
  15. Phillips AE, Waterer H et al (2015) Integer programming methods for large-scale practical classroom assignment problems. Comput Oper Res 53:42–53MathSciNetCrossRefzbMATHGoogle Scholar
  16. Riccio F, Patota F, Bella F, Borzi E, De Simone D, Suriani V. Iocchi L, Nardi D (2015) In: SPQR RoboCup 2015 Standard Platform League Team Description PaperGoogle Scholar
  17. Sander PV, Peleshchuk D et al. (2002) A scalable, distributed algorithm for efficient task allocation. AAMAS 1191–1198Google Scholar
  18. Sarratt T, Jhala A (2015) Rapid: a belief convergence strategy for collaborating with inconsistent agents. In: Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence. pp 1–6Google Scholar
  19. Scerri P, Farinelli A, Okamoto S et al (2005) Allocating tasks in extreme teams. In: Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems. ACM. pp 727–734Google Scholar
  20. Sheikhalishahi M, Wallace RM, Grandinetti L et al (2016) A multi-dimensional job scheduling. Future Gener Comput Syst 54:123–131CrossRefGoogle Scholar
  21. Skobelev P, Simonova E, Zhilyaev A (2016) Using multi-agent technology for the distributed management of a cluster of remote sensing satellites. Complex Syst: Fundament Appl 90:287CrossRefzbMATHGoogle Scholar
  22. Sun T, Xu Y, Improving He Q (2012) Search asynchronous, for distributed generalized assignment problem. In: International Conferences on IEEE/WIC/ACM, vol 2. pp 38–42Google Scholar
  23. Vera S, Cobano JA et al (2015) Collision avoidance for multiple UAVs using rolling-horizon policy. J Intelligent Robot Sys 1–10Google Scholar
  24. Xu Y, Scerri P, Sycara K et al (2005) An integrated token-based algorithm for scalable coordination. In: Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, pp 407–414Google Scholar
  25. Xu Y, Scerri P, Sycara K, Lewis M (2006) Comparing market and token-based coordination. In: Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems. ACM. pp 1113–1115Google Scholar
  26. Yeoh W, Yokoo M (2012) Distributed problem solving. AI Mag 33(3):53CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

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