Skip to main content
SpringerLink
Log in
Book cover

Progress in Advanced Computing and Intelligent Engineering pp 207–217Cite as

A Study on Some Aspects of Biologically Inspired Multi-agent Systems

  • Conference paper
  • First Online:

  • 1090 Accesses

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 564))

Abstract

Nature has always inspired human race in solving various problems. This chapter, in particular, shows a detailed overview of various biologically and nature-inspired multi-agent systems as proposed in the existing relevant literature. A detailed description and analysis of the proposed multi-agent strategies are provided. Multi-agent systems have been in various spheres of management. The applications in terms of cases and situations where these multi-agent systems are applicable are also mentioned along with the introduction of the various agent-based systems. The purpose of this chapter is to assist the readers to think about simulating the existing bio-inspired phenomena with the help of agent-based systems.

This is a preview of subscription content, log in via an institution.

References

  1. Bandyopadhyay, S., Bhattacharya, R.: Discrete and Continuous Simulation: Theory and Practice. CRC Press, Florida (2014)

    Book  MATH  Google Scholar 

  2. Gardner, H., Hatch, T.: Educational implications of the theory of multiple intelligences. Educ. Res. 18(8), 4–10 (1989)

    Google Scholar 

  3. Sternberg, R.: The Nature of Creativity: Contemporary Psychological Perspectives. Cambridge University Press (1988)

    Google Scholar 

  4. Perkins, D.: Outsmarting IQ: The Emerging Science of Learnable Intelligence. The Free Press, New York (1995)

    Google Scholar 

  5. García-Flores, R., Wang, X.Z.: A multi-agent system for chemical supply chain simulation and management support. OR Spectr. 24(3), 343–370 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  6. Giannakis, M., Louis, M.: A multi-agent based framework for supply chain risk management. J. Purchasing Supply Manag. 17(1), 23–31 (2011)

    Article  Google Scholar 

  7. Lou, P., Zhou, Z.-D., Chen, Y.-P., Ai, W.: Study on multi-agent-based agile supply chain management. Int. J. Adv. Manuf. Technol. 23(3–4), 197–203 (2004)

    Article  Google Scholar 

  8. Lee, J.-H., Kim, C.-O.: Multi-agent systems applications in manufacturing systems and supply chain management: a review paper. Int. J. Prod. Res. 46(1), 233–265 (2008)

    Article  MATH  Google Scholar 

  9. Williams, C.D., Biewener, A.A.: Pigeons trade efficiency for stability in response to level of challenge during confined flight. Proc. Natl. Acad. Sci. 112(11), 3392–3396 (2015)

    Article  Google Scholar 

  10. Bandyopadhyay, S., Bhattacharya, R.: On some aspects of nature-based algorithms to solve multi-objective problems. In: Yang, X.-S. (ed.) Artificial Intelligence, Evolutionary Computation and Metaheuristics. Studies in Computational Intelligence, vol. 427, pp. 477–524. Springer (2013). ISSN: 1860-949X, ISBN: 978-3-642-29693-2

    Google Scholar 

  11. Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)

    Article  Google Scholar 

  12. Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, USA (2004)

    MATH  Google Scholar 

  13. Christodoulou, S.: Construction imitating ants: resource-unconstrained scheduling with artificial ants. Autom. Constr. 18(3), 285–293 (2009)

    Article  Google Scholar 

  14. Xiang, W., Lee, H.P.: Ant colony intelligence in multi-agent dynamic manufacturing scheduling. Eng. Appl. Artif. Intell. 21(1), 73–85 (2008)

    Article  Google Scholar 

  15. Gunes, M., Sorges, U., Bouazizi, I.: ARA-the ant-colony based routing algorithm for MANETs. In: Proceedings of IEEE International Conference on Parallel Processing Workshops, pp. 79–85. Vancouver, B.C., Canada, 21 Aug 2002 (2002)

    Google Scholar 

  16. Bersini, H., Varela, F.J.: A variant of evolution strategies for vector optimization. In: Schwefel, H.-P., Männer, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 193–197. Springer, Heidelberg (1991)

    Google Scholar 

  17. Harmer, P.K., Williams, P.D., Gunsch, G.H., Lamont, G.B.: An artificial immune system architecture for computer security applications. IEEE Trans. Evol. Comput. 6(3), 252–280 (2002)

    Article  Google Scholar 

  18. Sathyanath, S., Sahin, F.: AISIMAM—An Artificial Immune System Based Intelligent Multi Agent Model and Its Application to a Mine Detection Problem. http://scholarworks.rit.edu/other/455 (2002)

  19. Dasgupta, D.: Immunity-based intrusion detection system: a general framework. In: Proceedings of the 22nd National Information Systems Security Conference, vol. 1, pp. 147–160. Hyatt Regency Hotel, Crystal City, VA, USA, 18 Oct 1999 (1999)

    Google Scholar 

  20. Castro, D., Leandro, N., Jon, T.: Artificial immune systems: a novel paradigm to pattern recognition. Artif. Neural Networks Pattern Recogn. 1, 67–84 (2002)

    Google Scholar 

  21. Eberhart, R.C., Kenndy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth Symposium on Micro Machine and Human Science, pp. 39–43. IEEE Service Center, Piscataway (1995)

    Google Scholar 

  22. Coello, C.A.C., Lamont, G.B., Veldhuizen, D.A.V.: Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd edn. Springer, USA (2007)

    MATH  Google Scholar 

  23. Kumar, R., Devendra, S., Abhinav, S.: A hybrid multi-agent based particle swarm optimization algorithm for economic power dispatch. Int. J. Electr. Power Energy Syst. 33(1), 115–123 (2011)

    Article  Google Scholar 

  24. Selvi, V., Umarani, R.: Comparative analysis of ant colony and particle swarm optimization techniques. Int. J. Comput. Appl. 5(4), 1–6 (2010)

    Google Scholar 

  25. Minar, N., Burkhart, R., Langton, C., Askenazi, M.: The swarm simulation system: a toolkit for building multi-agent simulations. Working Paper 96-06-042, Santa Fe Institute, Santa Fe (1996)

    Google Scholar 

  26. Cardon, A., Galinho, T., Vacher, J.-P.: Genetic algorithms using multi-objectives in a multi-agent system. Rob. Auton. Syst. 33, 179–190 (2000)

    Article  Google Scholar 

  27. Eguchi, T., Hirasawa, K., Hu, J., Ota, N.: A study of evolutionary multiagent models based on symbiosis. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 36(1), 179–193 (2006)

    Article  Google Scholar 

  28. Bandyopadhyay, S., Bhattacharya, R.: Finding optimum neighbor for routing based on multi-criteria, multi-agent and fuzzy approach. J. Intell. Manuf. 26(1), 25–42 (2015)

    Article  Google Scholar 

  29. Vincent, H.R., Ring, T.C.: Encyclopedia of Insects. Academic Press, USA (2003)

    Google Scholar 

  30. Roots, C.: Nocturnal Animals. Greenwood Press, London (2006)

    Google Scholar 

  31. Wilsdon, C.: Animal Behavior: Animal Defenses. Chelsea House Publishers, New York (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Business Administration, The University of Burdwan, Burdwan, 713104, West Bengal, India

    Gautam Mitra & Susmita Bandyopadhyay

Authors
  1. Gautam Mitra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Susmita Bandyopadhyay
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gautam Mitra .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science, Bialystok University of Technology, Białystok, Poland

    Khalid Saeed

  2. Dept. of Computer Science & Engg., University of Calcutta Dept. of Computer Science & Engg., Kolkata, West Bengal, India

    Nabendu Chaki

  3. C. V. Raman College of Engineering, Bhubaneswar, Odisha, India

    Bibudhendu Pati

  4. Department of Computer Science and Engineering, National Institute of Technology, Rourkela, Rourkela, Odisha, India

    Sambit Bakshi

  5. Department of Computer Science and Engineering, National Institute of Technology, Rourkela, Rourkela, Odisha, India

    Durga Prasad Mohapatra

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mitra, G., Bandyopadhyay, S. (2018). A Study on Some Aspects of Biologically Inspired Multi-agent Systems. In: Saeed, K., Chaki, N., Pati, B., Bakshi, S., Mohapatra, D. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 564. Springer, Singapore. https://doi.org/10.1007/978-981-10-6875-1_21

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-6875-1_21

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6874-4

  • Online ISBN: 978-981-10-6875-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation