Advertisement

Research of Task Allocation Strategy for Moving Image Matching Based on Multi-agent

  • Bin ShaoEmail author
  • Zhimin Yan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8971)

Abstract

In this paper moving image matching task allocation strategies based on the Multi-Agent system are discussed. Combined with genetic algorithm and simulated annealing algorithm, it gives out a new moving image pattern match-ing task allocation strategies based on the Multi-Agent system. Due to the unique nature of genetic algorithms and simulated annealing algorithm, makes the task allocation algorithm presented in this paper has new features that is different from the traditional method.

Keywords

Moving image matching Task allocation Genetic algorithm Simulated annealing algorithm 

References

  1. 1.
    Holland, J.H.: Adaptation in Natural and Artificial System. University of Michigan Press, Ann Ardor (1975)Google Scholar
  2. 2.
    Shao, B., Wang, G.: Judge of performance in the multi-agent system. Microelectron. Comput. 20(8), 80–81 (2003)Google Scholar
  3. 3.
    Shao, B., Jiang, Y., Shen, Q.: The multi-agent simulation and philosophy on economic game. J. Inf. Comput. Sci. 12(6), 2305–2309 (2009)Google Scholar
  4. 4.
    Mecar, I., Devlic, A.: Agent-oriented semantic discovery and matchmaking of Web services. In: 8th International Conference on Telecommunications- ConTEL, pp. 603–607 (2005)Google Scholar
  5. 5.
    Bin, L., Wen-Feng, L., Yu, Z.: Study on modeling of container terminal logistics system using agent-based computing and knowledge discovery. In: Proceedings of International Symposium and Advances in Computer and Sensor Networks and Systems, pp. 164–171. Aardvark Global Publishing, Slat Lake City, USA (2008)Google Scholar
  6. 6.
    Gajpal, Y., Rajendran, C., Ziegler, H.: An ant colony algorithm for scheduling in flowshops with sequence-dependent setup times of jobs. Int. J. Adv. Manuf. Technol. 30(5–6), 416–424 (2006)CrossRefGoogle Scholar
  7. 7.
    Ying, K.-C., Lin, S.-W.: Multi-heuristic desirability ant colony system heuristic for non-permutation flowshop scheduling problems. Int. J. Adv. Manuf. Technol. 33(7–8), 793–802 (2007)CrossRefGoogle Scholar
  8. 8.
    El-Menshawy, M., Bentahar, J., El-Kholy, W., Dssouli, R.: Verifying conformance of multi-agent commitment-based protocols. Expert Syst. Appl. 40, 122–138 (2013)CrossRefGoogle Scholar
  9. 9.
    Lomuscio, A., Qu, H., Solanki, M.: Towards verifying contract regulated service composition. Auton. Agent. Multi-Agent Syst. 24, 345–373 (2012)CrossRefGoogle Scholar
  10. 10.
    Telang, P., Singh, M.: Specifying and verifying cross-organizational business models: An agent oriented approach. IEEE Trans. Serv. Comput. 5, 305–318 (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.School of Information EngineeringHuzhou UniversityHuzhouChina
  2. 2.State Key Laboratory of CAD&CGZhejiang UniversityHangzhouChina
  3. 3.School of Teachers’ EducationHuzhou UniversityHuzhouChina

Personalised recommendations