Evaluation of a Self-organizing Heuristic for Interdependent Distributed Search Spaces

Christian Hinrichs, Michael Sonnenschein, Sebastian Lehnhoff

Abstract

Whenever multiple stakeholders try to optimize a common objective function in a distributed way, an adroit coordination mechanism is necessary. This contribution presents a formal model of distributed combinatorial optimization problems. Subsequently, a heuristic is introduced, that uses self-organizing mechanisms to optimize a common global objective as well as individual local objectives in a fully decentralized manner. This heuristic, COHDA2, is implemented in an asynchronous multi-agent system, and is being extensively evaluated by means of a real-world problem from the smart grid domain. We give insight into the convergence process and show the robustness of COHDA2 against unsteady communication networks. We show that COHDA2 is a very efficient decentralized heuristic that is able to tackle a distributed combinatorial optimization problem with regard to multiple local objective functions, as well as a common global objective function, without being dependent on centrally gathered knowledge.

References

  1. Bremer, J. and Sonnenschein, M. (2012). A distributed greedy algorithm for constraint-based scheduling of energy resources. In SEN-MAS'2012 Workshop, Proc. of the Federated Conference on Computer Science and Information Systems, pages 1285-1292, Wroclaw, Poland. IEEE Catalog Number CFP1285N-ART.
  2. Gellings, C. (2009). The Smart Grid: Enabling Energy Efficiency and Demand Response. The Fairmont Press, Inc.
  3. Gershenson, C. (2007). Design and Control of Selforganizing Systems. Copit-Arxives.
  4. Hinrichs, C., Lehnhoff, S., and Sonnenschein, M. (2012). A Decentralized Heuristic for Multiple-Choice Combinatorial Optimization Problems. In Operations Research Proceedings 2012 - Selected Papers of the International Conference on Operations Research (OR 2012), Hannover, Germany. Springer.
  5. Hirayama, K. and Yokoo, M. (1997). Distributed Partial Constraint Satisfaction Problem. In Principles and Practice of Constraint Programming, pages 222-236.
  6. Hölldobler, B. and Wilson, E. O. (1990). The Ants. Belknap Press of Harvard University Press.
  7. Jones, J. C., Myerscough, M. R., Graham, S., and Oldroyd, B. P. (2004). Honey bee nest thermoregulation: diversity promotes stability. Science (New York, N.Y.), 305(5682):402-4.
  8. Jordan, U. and Vajen, K. (2001). Influence Of The DHW Load Profile On The Fractional Energy Savings: A Case Study Of A Solar Combi-System With TRNSYS Simulations. Solar Energy, 69:197-208.
  9. Kaddoum, E. (2011). Optimization under Constraints of Distributed Complex Problems using Cooperative Self-Organization. Phd thesis, Université de Toulouse.
  10. Kok, J. K., Warmer, C. J., and Kamphuis, I. G. (2005). PowerMatcher. In Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems - AAMAS 7805, page 75, New York, New York, USA. ACM Press.
  11. Kroeker, K. L. (2011). Biology-inspired networking. Communications of the ACM, 54(6):11.
  12. Li, J., Poulton, G., and James, G. (2010). Coordination of Distributed Energy Resource Agents. Applied Artificial Intelligence, 24(5):351-380.
  13. Lust, T. and Teghem, J. (2012). The multiobjective multidimensional knapsack problem: a survey and a new approach. International Transactions in Operational Research, 19(4):495-520.
  14. Modi, P., Shen, W., Tambe, M., and Yokoo, M. (2005). ADOPT: Asynchronous Distributed Constraint Optimization with Quality Guarantees. Artificial Intelligence, 161(1-2):149-180.
  15. Penya, Y. (2006). Optimal Allocation and Scheduling of Demand in Deregulated Energy Markets. Phd, Vienna University of Technology.
  16. Pournaras, E., Warnier, M., and Brazier, F. M. (2010). Local agent-based self-stabilisation in global resource utilisation. International Journal of Autonomic Computing, 1(4):350.
  17. Reynolds, C. W. (1987). Flocks, herds and schools: A distributed behavioral model. SIGGRAPH Comput. Graph., 21(4):25-34.
  18. Serugendo, G., Gleizes, M.-P., and Karageorgos, A. (2005). Self-organisation in multi-agent systems. The Knowledge Engineering Review, 20(2):65-189.
  19. Strogatz, S. H. (2001). Exploring Complex Networks. Nature, 410(March):268-276.
  20. Tero, A., Takagi, S., Saigusa, T., Ito, K., Bebber, D. P., Fricker, M. D., Yumiki, K., Kobayashi, R., and Nakagaki, T. (2010). Rules for biologically inspired adaptive network design. Science (New York, N.Y.), 327(5964):439-42.
Download


Paper Citation


in Harvard Style

Hinrichs C., Sonnenschein M. and Lehnhoff S. (2013). Evaluation of a Self-organizing Heuristic for Interdependent Distributed Search Spaces . In Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8565-38-9, pages 25-34. DOI: 10.5220/0004227000250034


in Bibtex Style

@conference{icaart13,
author={Christian Hinrichs and Michael Sonnenschein and Sebastian Lehnhoff},
title={Evaluation of a Self-organizing Heuristic for Interdependent Distributed Search Spaces},
booktitle={Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2013},
pages={25-34},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004227000250034},
isbn={978-989-8565-38-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Evaluation of a Self-organizing Heuristic for Interdependent Distributed Search Spaces
SN - 978-989-8565-38-9
AU - Hinrichs C.
AU - Sonnenschein M.
AU - Lehnhoff S.
PY - 2013
SP - 25
EP - 34
DO - 10.5220/0004227000250034