Skip to main content
SpringerLink
Log in

A Review of Animal Behavior-Inspired Methods for Intelligent Systems

  • Conference paper
  • First Online:
Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016 (IntelliSys 2016)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 15))

Included in the following conference series:

Abstract

This paper examines the most significant approaches towards developing intelligent systems that were inspired by the behavior of natural animals. An overview is presented that describes the essential aspects of several of those methodologies and summarizes the most noteworthy related publications.

Emergent behaviors are those behaviors that arise out of complex system and cannot be explained by the specific rules of the system. Emergent behavior is observed from two aspects: those emerging within a single-agent and those occurring in multi-agent systems.

Popular algorithms inspired by bats, krills, and bees behavior are also reviewed. Their recent applications in solving optimization, data mining, scheduling, image processing, and similar difficult problems are listed.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Fister Jr., I., Yang, X., Fister, I., Brest, J., Fister, D.: A brief review of nature-inspired algorithms for optimization. ElektrotehniŠki Vestnik (2013)

    Google Scholar 

  2. Dorigo, M., Birattari, M., Stützle, T.: Ant colony optimization: artificial ants as a computational intelligence technique. IEEE Comput. Intell. Mag. 1(4), 28–39 (2006)

    Article  Google Scholar 

  3. Yang, X., Deb, S.: Cuckoo Search via Lévy Flights. In: Proceedings of World Congress on Nature & Biologically Inspired Computing, NaBIC 2009 (2009)

    Google Scholar 

  4. Ferreira, C.: Gene expression programming in problem solving. In: Soft Computing and Industry, pp. 635–653. Springer, London (2002)

    Google Scholar 

  5. Zhang, L., Dahlmann, C., Zhang, Y.: Human-inspired algorithms for continuous function optimization. In: 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems (2009)

    Google Scholar 

  6. Anderson, T., Donath, M.: Animal behavior as a paradigm for developing robot autonomy. Robot. Auton. Syst. 6(1–2), 145–168 (1990)

    Article  Google Scholar 

  7. Piperidis, S., Tsourveloudis, N.: Biomimetic behaviour based underwater control. In: 22nd Mediterranean Conference on Control and Automation (2014)

    Google Scholar 

  8. González, J., Pelta, D., Cruz, C., Terrazas, G., Krasnogor, N.: A new metaheuristic bat-inspired algorithm. In: Kacprzyk, J. (ed.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), vol. 284, pp. 65–74. Springer, Berlin (2010)

    Chapter  Google Scholar 

  9. Bahmani-Firouzi, B., Azizipanah-Abarghooee, R.: Optimal sizing of battery energy storage for micro-grid operation management using a new improved bat algorithm. Int. J. Electr. Power Energy Syst. 56, 42–54 (2014)

    Article  Google Scholar 

  10. Kashi, S., Minuchehr, A., Poursalehi, N., Zolfaghari, A.: Bat algorithm for the fuel arrangement optimization of reactor core. Ann. Nucl. Energy 64, 144–151 (2014)

    Article  Google Scholar 

  11. King, J., Spachis, A.: Scheduling: bibliography and review. Int. J. Phys. Distrib. Mater. Manag. 10(3), 103–132 (1980)

    Article  Google Scholar 

  12. Musikapun, P., Pongcharoen, P.: Solving multi-stage multi-machine multi-product scheduling problem using bat algorithm. In: 2nd International Conference on Management and Artificial Intelligence (2012)

    Google Scholar 

  13. Fister, I., Rauter, S., Yang, X., Ljubič, K., Fister, I.: Planning the sports training sessions with the bat algorithm. Neurocomputing 149, 993–1002 (2015)

    Article  Google Scholar 

  14. Zhang, J., Wang, G.: Image matching using a bat algorithm with mutation. AMM 203, 88–93 (2012)

    Article  Google Scholar 

  15. Alihodzic, A., Tuba, M.: Improved bat algorithm applied to multilevel image thresholding. Sci. World J. 2014, 1–16 (2014)

    Article  Google Scholar 

  16. Akhtar, S., Ahmad, A., Abdel-Rahman, E.: A metaheuristic bat-inspired algorithm for full body human pose estimation. In: 2012 Ninth Conference on Computer and Robot Vision (2012)

    Google Scholar 

  17. Gandomi, A., Alavi, A.: Krill Herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 17(12), 4831–4845 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  18. Hofmann, E., Haskell, A., Klinck, J., Lascara, C.: Lagrangian modelling studies of antarctic krill (Euphausia superba) swarm formation. ICES J. Mar. Sci. 61(4), 617–631 (2004)

    Article  Google Scholar 

  19. Mandal, B., Roy, P., Mandal, S.: Economic load dispatch using Krill Herd algorithm. Int. J. Electr. Power Energy Syst. 57, 1–10 (2014)

    Article  Google Scholar 

  20. Roy, P., Paul, C.: Optimal power flow using Krill Herd algorithm. Int. Trans. Electr. Energy Syst. 25(8), 1397–1419 (2014)

    Article  Google Scholar 

  21. Sur, C., Shukla, A.: Discrete Krill Herd algorithm – a bio-inspired meta-heuristics for graph based network route optimization. In: Distributed Computing and Internet Technology, pp. 152–163. Springer, Switzerland (2014)

    Google Scholar 

  22. Kalaiselvi, D., Radhakrishnan, R.: Multiconstrained QoS routing using a differentially guided Krill Herd algorithm in mobile ad hoc networks. Math. Problems Eng. 2015, 1–10 (2015)

    Article  Google Scholar 

  23. Lari, N., Abadeh, M.: Training artificial neural network by Krill-Herd algorithm. In: 2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference (2014)

    Google Scholar 

  24. Faris, H., Aljarah, I., Alqatawna, J.: Optimizing feedforward neural networks using Krill Herd algorithm for e-mail spam detection. In: 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT) (2015)

    Google Scholar 

  25. Li, Z., Yi, J., Wang, G.: A new swarm intelligence approach for clustering based on Krill Herd with elitism strategy. Algorithms 8(4), 951–964 (2015)

    Article  MathSciNet  Google Scholar 

  26. Nikbakht, H., Mirvaziri, H.: A new clustering approach based on K-means and Krill Herd algorithm. In: 2015 23rd Iranian Conference on Electrical Engineering (2015)

    Google Scholar 

  27. Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes Univ. Press (2005)

    Google Scholar 

  28. Tereshko, V., Loengarov, A.: Collective decision-making in honey bee foraging dynamics. Comp. Inf. Syst. J. 9(3), 1352–9404 (2005)

    Google Scholar 

  29. Yan, X., Zhu, Y., Zou, W., Wang, L.: A new approach for data clustering using hybrid artificial bee colony algorithm. Neurocomputing 97, 241–250 (2012)

    Article  Google Scholar 

  30. Karaboga, D., Ozturk, C.: Fuzzy clustering with artificial bee colony algorithm. Sci. Res. Essays 5(14), 1899–1902 (2010)

    Google Scholar 

  31. Zhang, C., Ouyang, D., Ning, J.: An artificial bee colony approach for clustering. Expert Syst. Appl. 37(7), 4761–4767 (2010)

    Article  Google Scholar 

  32. Pan, Q., Tasgetiren, M., Suganthan, P., Chua, T.: A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Inf. Sci. 181(12), 2455–2468 (2011)

    Article  MathSciNet  Google Scholar 

  33. Li, J., Pan, Q., Gao, K.: Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems. Int. J. Adv. Manuf. Technol. 55(9–12), 1159–1169 (2011)

    Article  Google Scholar 

  34. Tasgetiren, M., Pan, Q., Suganthan, P., Chen, A.: A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops. Inf. Sci. 181(16), 3459–3475 (2011)

    Article  MathSciNet  Google Scholar 

  35. Karaboga, D., Okdem, S., Ozturk, C.: Cluster based wireless sensor network routing using artificial bee colony algorithm. Wirel. Netw. 18(7), 847–860 (2012)

    Article  Google Scholar 

  36. Szeto, W., Wu, Y., Ho, S.: An artificial bee colony algorithm for the capacitated vehicle routing problem. Eur. J. Oper. Res. 215(1), 126–135 (2011)

    Article  Google Scholar 

  37. Ma, M., Liang, J., Guo, M., Fan, Y., Yin, Y.: SAR image segmentation based on artificial bee colony algorithm. Appl. Soft Comput. 11(8), 5205–5214 (2011)

    Article  Google Scholar 

  38. Yildiz, A.: A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing. Appl. Soft Comput. 13(5), 2906–2912 (2013)

    Article  Google Scholar 

  39. Sonmez, M.: Discrete optimum design of truss structures using artificial bee colony algorithm. Struct. Multidiscip. Optim. 43(1), 85–97 (2010)

    Article  MathSciNet  Google Scholar 

  40. Ayan, K., Kılıç, U.: Artificial bee colony algorithm solution for optimal reactive power flow. Appl. Soft Comput. 12(5), 1477–1482 (2012)

    Article  Google Scholar 

  41. Öztürk, C., Karaboga, D., Görkemli, B.: Artificial bee colony algorithm for dynamic deployment of wireless sensor networks. Turk. J. Elec. Eng. Comp. Sci. 20(2), (2012)

    Google Scholar 

  42. Lee, J., Ahn, C., An, J.: A honey bee swarm-inspired cooperation algorithm for foraging swarm robots: an empirical analysis. In: 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (2013)

    Google Scholar 

  43. Babu, D., Krishna, P.: Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl. Soft Comput. 13(5), 2292–2303 (2013)

    Article  Google Scholar 

  44. Brooks, R.: A robust layered control system for a mobile robot. IEEE J. Robot. Autom. 2(1), 14–23 (1986)

    Article  Google Scholar 

  45. Rosenblatt, J.K., Payton, D.W.: A fine-grained alternative to the subsumption architecture for mobile robot control. In: International Joint Conference on Neural Networks, 1989. IJCNN, Washington, DC, USA, vol. 2, pp. 317–323 (1989)

    Google Scholar 

  46. Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. In: 1985 IEEE International Conference on Robotics and Automation. Proceedings, pp. 500–505 (1985)

    Google Scholar 

  47. Li, Z., Sim, C.H., Hean Low, M.Y.: A survey of emergent behavior and its impacts in agent-based systems. In: 2006 IEEE International Conference on Industrial Informatics, Singapore, pp. 1295–1300 (2006)

    Google Scholar 

  48. Ehrenfeld, S., Schrodt, F., Butz, M.V.: Mario lives! An adaptive learning AI approach for generating a living and conversing Mario agent. In: 2015 Video Proceedings of the AAAI Video Competition, Austin, TX, USA (2015)

    Google Scholar 

  49. Schrodt, F., Lohmann, J., Butz, M.V.: Mario becomes social! In: 2016 Video Proceedings of the AAAI Video Competition, Phoenix, AZ, USA (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Informatics, Heilbronn University, Heilbronn, Germany

    Glorian Yapinus & Ruben Nuredini

Authors
  1. Glorian Yapinus
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruben Nuredini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Glorian Yapinus or Ruben Nuredini .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Engineering, School of Computing and Mathematics, University of Ulster at Jordanstown, Newtownabbey, United Kingdom

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Rahul Bhatia

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Yapinus, G., Nuredini, R. (2018). A Review of Animal Behavior-Inspired Methods for Intelligent Systems. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. IntelliSys 2016. Lecture Notes in Networks and Systems, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-319-56994-9_60

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-56994-9_60

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56993-2

  • Online ISBN: 978-3-319-56994-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation