Logo PTI Logo FedCSIS

Proceedings of the 16th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 25

Dynamic communication topologies for distributed heuristics in energy system optimization algorithms

,

DOI: http://dx.doi.org/10.15439/2021F60

Citation: Proceedings of the 16th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 25, pages 191–200 ()

Full text

Abstract. The communication topology is an essential aspect in designing distributed optimization heuristics. It can influence the exploration and exploitation of the search space and thus the optimization performance in terms of solution quality, convergence speed and collaboration costs -- relevant aspects for applications operating critical infrastructure in energy systems. In this work, we present an approach for adapting the communication topology during runtime, based on the principles of simulated annealing. We compare the approach to common static topologies regarding the performance of an exemplary distributed optimization heuristic. Finally, we investigate the correlations between fitness landscape properties and defined performance metrics.

References

  1. P. Vrba, V. Mařík, P. Siano, P. Leitão, G. Zhabelova, V. Vyatkin, and T. Strasser, “A Review of Agent and Service-Oriented Concepts Applied to Intelligent Energy Systems,” IEEE Transactions of Industrial Informatics, vol. 10, no. 3, pp. 1890–1903, 2014.
  2. S. Ramchurn, P. Vytelingum, A. Rogers, and N. Jennings, “Agent-based homeostatic control for green energy in the smart grid,” ACM Transactions on Intelligent Systems and Technology, vol. 2, 2011. [Online]. Available: http://dx.doi.org/10.1145/1989734.1989739
  3. M. Sonnenschein, C. Hinrichs, A. Nieße, and U. Vogel, “Supporting Renewable Power Supply through Distributed Coordination of Energy Resources,” in ICT Innovations for Sustainability, 1st ed., L. M. Hilty and B. Aebischer, Eds. Berlin: Springer, Cham, 2015, vol. 1, pp. 387–404.
  4. A. Nieße, M. Tröschel, and M. Sonnenschein, “Designing Dependable and Sustainable Smart Grids – How to Apply Algorithm Engineering to Distributed Control in Power Systems,” Environmental Modelling & Software, 2013.
  5. J. Hu, A. Saleem, S. You, L. Nordström, M. Lind, and J. Østergaard, “A multi-agent system for distribution grid congestion management with electric vehicles,” Engineering Applications of Artificial Intelligence, vol. 38, pp. 45–58, 2015. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0952197614002577
  6. J. Lai, X. Lu, A. Monti, and R. W. de Doncker, “Agent-based voltage regulation scheme for active distributed networks under distributed quantized communication,” in IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society, vol. 1, 2019, pp. 5941–5946.
  7. A. Nieße, N. Ihle, S. Balduin, M. Postina, M. Tröschel, and S. Lehnhoff, “Distributed ledger technology for fully automated congestion management,” Energy Informatics, vol. 1, no. 1, p. 22, 2018. [Online]. Available: https://doi.org/10.1186/s42162-018-0033-3
  8. K. Kok, C. Warmer, R. Kamphuis, P. Mellstrand, and R. Gustavsson, “Distributed Control in the Electricity Infrastructure,” in Proceedings of the International Conference on Future Power Systems, 2005.
  9. S. Lehnhoff, O. Krause, C. Rehtanz, and H. F. Wedde, “Distributed Autonomous Power Management,” at - Automatisierungstechnik, vol. 3, pp. 167 – 179, 2011.
  10. D. K. Molzahn, F. Dörfler, H. Sandberg, S. H. Low, S. Chakrabarti, R. Baldick, and J. Lavaei, “A survey of distributed optimization and control algorithms for electric power systems,” IEEE Transactions on Smart Grid, vol. 8, no. 6, pp. 2941–2962, 2017.
  11. Y. Rizk, M. Awad, and E. W. Tunstel, “Decision making in multiagent systems: A survey,” IEEE Transactions on Cognitive and Developmental Systems, vol. 10, no. 3, pp. 514–529, 2018.
  12. S. Holly and A. Nieße, “On the effects of communication topologies on the performance of distributed optimization heuristics in smart grids,” in INFORMATIK 2020, R. H. Reussner, A. Koziolek, and R. Heinrich, Eds. Gesellschaft für Informatik, Bonn, 2021, pp. 783–794.
  13. E.-G. Talbi, Metaheuristics: From Design to Implementation. John Wiley & Sons, 2009, vol. 74.
  14. M. Ruciński, D. Izzo, and F. Biscani, “On the impact of the migration topology on the island model,” Parallel Computing, vol. 36, no. 10-11, pp. 555–571, 2010.
  15. T. Crainic, “Parallel metaheuristics and cooperative search,” in Handbook of Metaheuristics. Springer, 2019, pp. 419–451.
  16. M. Hijaze and D. Corne, “An investigation of topologies and migration schemes for asynchronous distributed evolutionary algorithms,” in 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC). IEEE, 2009, pp. 636–641.
  17. ——, “Distributed evolutionary algorithm topologies with adaptive migration schemes,” in 2011 IEEE Congress of Evolutionary Computation (CEC). IEEE, 2011, pp. 608–615.
  18. M. Sanu and G. Jeyakumar, “Empirical performance analysis of distributed differential evolution for varying migration topologies,” International Journal of Applied Engineering Research, vol. 10, no. 5, pp. 11–919, 2015.
  19. T.-C. Wang, C.-Y. Lin, R.-T. Liaw, and C.-K. Ting, “Empirical analysis of island model on large scale global optimization,” in 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2019, pp. 342–349.
  20. J. Momin and X.-S. Yang, “A literature survey of benchmark functions for global optimization problems,” Int. Journal of Mathematical Modelling and Numerical Optimisation, vol. 4, no. 2, pp. 150–194, 2013.
  21. X. Li, K. Tang, M. N. Omidvar, Z. Yang, K. Qin, and H. China, “Benchmark functions for the cec 2013 special session and competition on large-scale global optimization,” gene, vol. 7, no. 33, p. 8, 2013.
  22. C. Blum and A. Roli, “Metaheuristics in combinatorial optimization: Overview and conceptual comparison,” ACM computing surveys (CSUR), vol. 35, no. 3, pp. 268–308, 2003.
  23. D. H. Wolpert and W. G. Macready, “No free lunch theorems for optimization,” IEEE transactions on evolutionary computation, vol. 1, no. 1, pp. 67–82, 1997.
  24. Y. Sun, S. K. Halgamuge, M. Kirley, and M. A. Munoz, “On the selection of fitness landscape analysis metrics for continuous optimization problems,” in 7th International Conference on Information and Automation for Sustainability. IEEE, 2014, pp. 1–6.
  25. K. M. Malan and A. P. Engelbrecht, “Quantifying ruggedness of continuous landscapes using entropy,” in 2009 IEEE Congress on evolutionary computation. IEEE, 2009, pp. 1440–1447.
  26. V. K. Vassilev, T. C. Fogarty, and J. F. Miller, “Information characteristics and the structure of landscapes,” Evolutionary computation, vol. 8, no. 1, pp. 31–60, 2000.
  27. ——, “Smoothness, ruggedness and neutrality of fitness landscapes: from theory to application,” Advances in Evolutionary Computing: Theory and Applications, p. 3, 2002.
  28. K. M. Malan and A. P. Engelbrecht, “Ruggedness, funnels and gradients in fitness landscapes and the effect on pso performance,” in 2013 IEEE Congress on Evolutionary Computation. IEEE, 2013, pp. 963–970.
  29. M. Lunacek and D. Whitley, “The dispersion metric and the cma evolution strategy,” in Proceedings of the 8th annual conference on Genetic and evolutionary computation, 2006, pp. 477–484.
  30. M. Locatelli, “A note on the griewank test function,” Journal of global optimization, vol. 25, no. 2, pp. 169–174, 2003.
  31. C. Hinrichs and M. Sonnenschein, “A distributed combinatorial optimisation heuristic for the scheduling of energy resources represented by self-interested agents.” IJBIC, vol. 10, no. 2, pp. 69–78, 2017.
  32. J. Bremer and S. Lehnhoff, “An agent-based approach to decentralized global optimization-adapting cohda to coordinate descent,” in International Conference on Agents and Artificial Intelligence, vol. 2. SCITEPRESS, 2017, pp. 129–136.
  33. H.-G. Beyer and S. Finck, “Happycat–a simple function class where well-known direct search algorithms do fail,” in International conference on parallel problem solving from nature. Springer, 2012, pp. 367–376.
  34. L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen, Classification and regression trees. CRC press, 1984.
  35. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
  36. A. Nieße, J. Bremer, and S. Lehnhoff, “On local minima in distributed energy scheduling.” in FedCSIS (Position Papers), 2017, pp. 61–68.