Measuring Adaptability of ”Swarm Intelligence” for Resource Scheduling and Optimization in Real Time

Petr Skobelev, Igor Mayorov, Sergey Kozhevnikov, Alexander Tsarev, Elena Simonova

2015

Abstract

In this paper modern methods of scheduling and resource optimization based on the holonic approach and principles of “Swarm Intelligence” are considered. The developed classes of holonic agents and method of adaptive real time scheduling where every agent is connected with individual satisfaction function by the set of criteria and bonus/penalty function are discussed. In this method the plan is considered as a un-stable equilibrium (consensus) of agents interests in dynamically self-organized network of demands and supply agents. The self-organization of plan demonstrates a “swarm intelligence” by spontaneous autocatalitical reactions and other not-linear behaviours. It is shown that multi-agent technology provides a generic framework for developing and researching various concepts of “Swarm Intelligence” for real time adaptive event-driving scheduling and optimization. The main result of research is the developed approach to evaluate the adaptability of “Swarm Intelligence” by measuring improve of value and transition time from one to another unstable state in case of disruptive events processing. Measuring adaptability helps to manage self-organized systems and provide better quality and efficiency of real time scheduling and optimization. This approach is under implementation in multi-agent platform for adaptive resource scheduling and optimization. The results of first experiments are presented and future steps of research are discussed.

References

  1. Rzevski, G., Skobelev P., 2014. Managing complexity. WIT Press. Boston.
  2. Park, A., Nayyar, G., Low, P., 2014. Supply Chain Perspectives and Issues. A Literature Review, April 21. Fung Global Institute and World Trade Organization.
  3. Mohammadi, A., Akl, S., 2005. Scheduling Algorithms for Real-Time Systems. Technical Report, no. 2005-499. School of Computing Queen's University, Kingston.
  4. Joseph, M., 2001. Real-time Systems: Specification, Verification and Analysis. Prentice Hall.
  5. Pinedo, M., 2008. Scheduling: Theory, Algorithms, and Systems. Springer.
  6. Leung, J., 2004. Handbook of Scheduling: Algorithms, Models and Performance Analysis. CRC Computer and Information Science Series. Chapman & Hall.
  7. Binitha, S., Sathya, S., 2012. A Survey of Bio inspired Optimization Algorithms. Int. Journal of Soft Computing and Engineering, vol. 2, issue 2, pp. 2231- 2307.
  8. Laha, D., 2008. Heuristics and Metaheuristics for Solving Scheduling Problems. In: Hand-book of Computational Intelligence in Manufacturing and Production Management. Idea Group Reference.
  9. Burke, E. K. et al., 2013. Hyper-heuristics: a survey of the state of the art. Journal of the Operational Research Society, vol. 64, pp. 1695-1724.
  10. Leitao, P, Vrba, P., 2011. Recent Developments and Future Trends of Industrial Agents. In HoloMAS 2011, 5th International Conference on Holonic and MultiAgent Systems. Springer, Berlin, pp. 15-28.
  11. Skobelev, P., 2014. Multi-Agent Systems for Real Time Adaptive Resource Management. In Industrial Agents: Emerging Applications of Software Agents in Industry. Elsevier.
  12. Brussel, H. V., Wyns, J., Valckenaers, P., Bongaerts, L., 1998. Reference architecture for holonic manufacturing systems: PROSA. Computer in Industry, vol. 37, no. 3, pp. 255-274.
  13. Vittikh, V., Skobelev, P., 2003. Multiagent Interaction Models for Constructing the Demand-Resource Networks in Open Systems. Automation and Remote Control, vol. 64, issue 1, pp. 162-169.
  14. Kennedy, J., Eberhart, R., 1995. Particle swarm optimization. In IEEE International Conference on Neural Networks, vol. 4, pp. 1942-1948. IEEE.
  15. Magnus, E. Hvass, P., 2010. Good Parameters for Particle Swarm Optimization, Technical Report, no. HL1001. Hvass Laboratories.
  16. Dong, T., 2013. A Review of Convergence Analysis of Particle Swarm Optimization. Int. Journal of Grid and Distributed Computing, vol.6, no.6, pp.117-128.
  17. Imrana, M. et al., 2013. An Overview of Particle Swarm Optimization Variants. Procedia Engineering, vol. 53, pp. 491-496. Elseiver.
  18. Oliinyk, A., 2011. The Multiagent Optimization Method with Adaptive Parameters. Artificial Intelligence journal, no.1, pp. 83-90.
  19. Sun, S., Li, J., 2014. A two-swarm cooperative particle swarms optimization. Swarm and Evolutionary Computation, vol. 15, pp. 1-18. Elseiver.
  20. Tasgetiren, M. et al., 2008. Particle swarm optimization and differential evolution algorithms for job shop scheduling problem. International Journal of Operational Research, vol. 3, no. 2, pp. 120-135.
  21. Skobelev, P., 2010. Bio-Inspired Multi-Agent Technology for Industrial Applications. Multi-Agent Systems - Modeling, Control, Programming, Simulations and Applications. Faisal Alkhateeb (Ed.). InTech Publishers. Austria. 28 p.
  22. Skobelev, P. et al., 2014. Practical Approach and MultiAgent Platform for Designing Real Time Adaptive Scheduling Systems. In Lecture Notes in Computer Science series, vol. 8473, pp. 1-12. Springer, Switzerland.
  23. Zadeh L. A., 1963. On the definition of adaptivity. In Proc. IEEE, vol. 51, pp. 469-470. IEEE.
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Paper Citation


in Harvard Style

Skobelev P., Mayorov I., Kozhevnikov S., Tsarev A. and Simonova E. (2015). Measuring Adaptability of ”Swarm Intelligence” for Resource Scheduling and Optimization in Real Time . In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-074-1, pages 517-522. DOI: 10.5220/0005276605170522


in Bibtex Style

@conference{icaart15,
author={Petr Skobelev and Igor Mayorov and Sergey Kozhevnikov and Alexander Tsarev and Elena Simonova},
title={Measuring Adaptability of ”Swarm Intelligence” for Resource Scheduling and Optimization in Real Time},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2015},
pages={517-522},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005276605170522},
isbn={978-989-758-074-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Measuring Adaptability of ”Swarm Intelligence” for Resource Scheduling and Optimization in Real Time
SN - 978-989-758-074-1
AU - Skobelev P.
AU - Mayorov I.
AU - Kozhevnikov S.
AU - Tsarev A.
AU - Simonova E.
PY - 2015
SP - 517
EP - 522
DO - 10.5220/0005276605170522