Skip to main content
SpringerLink
Log in

On artificial immune systems and swarm intelligence

  • Published:
Swarm Intelligence Aims and scope Submit manuscript

Abstract

This position paper explores the nature and role of two bio-inspired paradigms, namely Artificial Immune Systems (AIS) and Swarm Intelligence (SI). We argue that there are many aspects of AIS that have direct parallels with SI and examine the role of AIS and SI in science and also in engineering, with the primary focus being on the immune system. We explore how in some ways, algorithms from each area are similar, but we also advocate, and explain, that rather than being competitors, AIS and SI are complementary tools and can be used effectively together to solve complex engineering problems.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Aggarwal, C., Hinneburg, A., & Keim, D. (2001). On the surprising behavior of distance metrics in high dimensional space. In J. Van den Bussche & V. Vianu (Eds.), Lecture notes in computer science : Vol. 1973. Database theory—ICDT 2001, 8th international conference, proceedings (pp. 420–434). Berlin: Springer.

    Google Scholar 

  • Andrews, P. S. (2008). An investigation of a methodology for the development of artificial immune systems: A case study in receptor degeneracy. PhD thesis, Department of Computer Science, University of York, UK.

  • Arvind, D., & Wong, K. (2004). Speckled computing: Disruptive technology for networked information appliances. In IEEE international symposium on consumer electronics 2004, ISCE 2004 (pp. 219–223). Piscataway: IEEE Press.

    Chapter  Google Scholar 

  • Bernaschi, M., & Castiglione, F. (2001). Design and implementation of an immune system simulator. Computers in Biology and Medicine, 31, 303–331.

    Article  Google Scholar 

  • Bersini, H. (2001). Self-assertion versus self-recognition: A tribute to Francisco Varela. In J. Timmis & P. J. Bentley (Eds.), Proceedings of the first international conference on artificial immune systems, ICARIS 2002 (pp. 107–112). Kent: University of Kent Printing Unit.

    Google Scholar 

  • Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm intelligence: From natural to artificial systems. New York: Oxford University Press.

    MATH  Google Scholar 

  • Chtanova, T., Schaeffer, M., Han, S., van Dooren, G., Nollmann, M., Herzmark, P., Chan, S. W., Satija, H., Camfield, K., Aaron, H., Striepen, B., & Robey, E. (2008). Dynamics of neutrophil migration in lymph nodes during infection. Immunity, 29(3), 487–496.

    Article  Google Scholar 

  • Coelho, G., & Von Zuben, F. J. (2006). omni-aiNet: An immune-inspired approach for omni optimization. In H. Bersini & J. Carneiro (Eds.), Lecture notes in computer science : Vol. 4163. Artificial immune systems, 5th international conference, ICARIS 2006 (pp. 294–308). Berlin: Springer.

    Chapter  Google Scholar 

  • Cohen, I. R. (2000). Tending Adam’s garden: Evolving the cognitive immune self. London: Elsevier Academic Press.

    Google Scholar 

  • Davoudani, D., & Hart, E. (2008). Computing the state of specknets: An immune inspired approach. In International symposium on performance evaluation of computer and telecommunication systems, 2008, SPECTS 2008 (pp. 52–59). Piscataway: IEEE Press.

    Google Scholar 

  • Davoudani, D., Hart, E., & Paechter, B. (2007). An immune-inspired approach to speckled computing. In L. N. de Castro, F. J. Von Zuben, & H. Knidel (Eds.), Lecture notes in computer science : Vol. 4628. Artificial immune systems, 6th international conference, ICARIS 2007 (pp. 288–299). Berlin: Springer.

    Chapter  Google Scholar 

  • Davoudani, D., Hart, E., & Paechter, B. (2008). Computing the state of specknets: Further analysis of an innate immune-inspired model. In P. J. Bentley, D. Lee, & S. Jung (Eds.), Lecture notes in computer science : Vol. 5132. Artificial immune systems, 7th international conference, ICARIS 2008 (pp. 95–106). Berlin: Springer.

    Chapter  Google Scholar 

  • de Castro, L. N., & Timmis, J. (2002a). An artificial immune network for multimodal function optimization. In IEEE congress on evolutionary computation, 2002, CEC 2002 (pp. 699–704). Piscataway: IEEE Press.

    Google Scholar 

  • de Castro, L. N., & Timmis, J. (2002b). Artificial immune systems: A new computational intelligence approach. London: Springer.

    MATH  Google Scholar 

  • de Castro, L. N., & Zuben, F. J. V. (2001). aiNet: An artificial immune network for data analysis. In H. A. Abbass, R. A. Sarker, & C. S. Newton (Eds.), Data mining: A heuristic approach (pp. 231–259). Hershey: Idea Group Publishing.

    Google Scholar 

  • de Lemos, R., Timmis, J., Forrest, S., & Ayara, M. (2007). Immune-inspired adaptable error detection for automated teller machines. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 37(5), 873–886.

    Article  Google Scholar 

  • Dembic, Z. (2004). Response to Cohn: The immune system rejects the harmful, protects the useful and neglects the rest of microorganisms. Scandinavian Journal of Immunology, 60, 3–5.

    Article  Google Scholar 

  • Deneubourg, J. L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., & Chretien, L. (1991). The dynamics of collective sorting: Robot-like ants and ant-like robots. In Proceedings of the first international conference on simulation of adaptive behavior: From animals to animats (pp. 356–365). Cambridge: MIT Press.

    Google Scholar 

  • Dilger, W., & Strangfeld, S. (2006). Properties of the Bersini experiment on self-assertion. In Genetic and evolutionary computation conference, GECCO 2006, proceedings (pp. 95–102). New York: ACM.

    Chapter  Google Scholar 

  • Dorigo, M., & Birattari, M. (2007). Swarm intelligence. Scholarpedia, 2(9), 1462.

    Article  Google Scholar 

  • Edelman, G. M., & Gally, JA (2001). Degeneracy and complexity in biological systems. Proceedings of the National Academy of Science (PNAS), 98(24), 13,763–13,768.

    Google Scholar 

  • Egen, J. G., Rothfuchs, A. G., Feng, C. G., Winter, N., Sher, A., & Germain, R. N. (2008). Macrophage and T cell dynamics during the development and disintegration of mycobacterial granulomas. Immunity, 28, 271–284.

    Article  Google Scholar 

  • Farmer, J. D., Packard, N. H., & Perelson, A. S. (1986). The immune system, adaptation, and machine learning. Physica D, 22, 187–204.

    MathSciNet  Google Scholar 

  • Forrest, S., & Beauchemin, C. (2007). Computer immunology. Immunological Reviews, 216(1), 176–197.

    Google Scholar 

  • Freitas, A., & Timmis, J. (2007). Revisiting the foundations of artificial immune systems for data mining. IEEE Transactions on Evolutionary Computing, 11(4), 521–540.

    Article  Google Scholar 

  • Garnier, S., Gautrais, J., & Theraulaz, G. (2007). The biological principles of swarm intelligence. Swarm Intelligence, 1(1), 3–31.

    Article  Google Scholar 

  • Garrett, S. (2005). How do we evaluate artificial immune systems? Evolutionary Computation, 13(2), 145–177.

    Article  Google Scholar 

  • Gazi, V., & Passino, K. M. (2003). Stability analysis of swarms. IEEE Transactions on Automatic Control, 48, 692–697.

    Article  MathSciNet  Google Scholar 

  • Greensmith, J., Aickelin, U., & Tedesco, J. (2010). Information fusion for anomaly detection with the dendritic cell algorithm. Information Fusion, 11(1), 21–34.

    Article  Google Scholar 

  • Grosan, C., Abraham, A., & Chis, M. (2006). Swarm intelligence in data-mining. Studies in Computational Intelligence (SCI), 34, 1–20.

    Article  Google Scholar 

  • Halloy, J., Sempo, G., Caprari, G., Rivault, C., Asadpour, M., Tache, F., Durier, V., Said, I., Canonge, S., Ame, J., Detrain, C., Correll, N., Martinoli, A., Mondada, F., Siegwart, R., & Deneubourg, J. (2007). Social integration of robots into groups of cockroaches to control self-organized choices. Science, 318(5853), 1155–1158.

    Article  Google Scholar 

  • Handl, J., Knowles, J., & Dorigo, M. (2006). Ant-based clustering and topographical mapping. Artificial Life, 12, 36–61.

    Article  Google Scholar 

  • Hart, E. (2005). Not all balls are round: An investigation of alternative recognition-region shapes. In C. Jacob, M. L. Pilat, P. J. Bentley, & J. Timmis (Eds.), Lecture notes in computer science : Vol. 3627. Artificial immune systems, 4th international conference, ICARIS 2005 (pp. 29–42). Berlin: Springer.

    Google Scholar 

  • Hart, E., & Davoudani, D. (2009). Dendritic cell trafficking: From immunology to engineering. In P. S. Andrews, J. Timmis, N. Owens, U. Aickelin, E. Hart, A. Hone, & A. Tyrrell (Eds.), Lecture notes in computer science : Vol. 5666. Artificial immune systems, 8th international conference, ICARIS 2009 (pp. 11–13). Berlin: Springer.

    Chapter  Google Scholar 

  • Hart, E., & Timmis, J. (2008). Application areas of AIS: The past, the present and the future. Applied Soft Computing, 8(1), 191–201.

    Article  Google Scholar 

  • Hart, E., Bersini, H., & Santos, F. (2007). How affinity influences tolerance in an idiotypic network. Journal of Theoretical Biology, 249(3), 422–436.

    Article  Google Scholar 

  • Hart, E., Bersini, H., & Santos, F. (2009). Structure vs function: A topological perspective on immune networks. Natural Computing. doi:10.1007/s11047-009-9138-8.

    Google Scholar 

  • Hoffmeyer, J. (1997). The swarming body. In Semiotics around the world, proceedings of the fifth congress of the international association for semiotic studies (pp. 937–940). Berkeley: Mouton de Gruyter.

    Google Scholar 

  • Humza, R., Scholz, O., Mokhtar, M., Timmis, J., & Tyrrell, A. (2009). Towards energy homeostasis in an autonomous self-reconfigurable modular robotic organism. In Comptation world 2009 (pp. 21–26). Piscataway: IEEE Press.

    Google Scholar 

  • Jacob, C., Steil, S., & Bergmann, K. P. (2006). The swarming body: Simulating the decentralized defenses of immunity. In H. Bersini & J. Carneiro (Eds.), Lecture notes in computer science : Vol. 4163. Artificial immune systems, 5th international conference, ICARIS 2006 (pp. 52–65). Berlin: Springer.

    Chapter  Google Scholar 

  • Janeway, C., & Medzhitov, R. (2002). Innate immune recognition. Annual Review of Immunology, 20, 197–216.

    Article  Google Scholar 

  • Janeway, C. A., Travers, P., Walport, M., & Shlomchik, M. J. (2001). Immunobiology (5th ed.). New York: Garland Publishing.

    Google Scholar 

  • Kennedy, J., & Eberhart, R. (2001). Swarm intelligence. San Francisco: Morgan Kaufmann.

    Google Scholar 

  • Kennedy, J., & Mendes, R. (2002). Population structure and particle swarm performance. In IEEE congress on evolutionary computation, 2002, CEC 2002 (pp. 1671–1676). Piscataway: IEEE Press.

    Google Scholar 

  • Kernbach, S., Scholz, O., Harada, K., Popesku, S., Leidke, J., Raja, H., Liu, W., Caparrelli, F., Jemai, J., Havlik, J., Meister, E., & Levi, P. (2010). Multi-robot organisms: State of the art. In K. Koy, R. Nagpal, & W. Shen (Eds.), IEEE international conference on robotics and automation (workshop on modular robotics) (pp. 1–10). Piscataway: IEEE Press.

    Google Scholar 

  • Kleinstein, P., & Seiden, S. H. (2000). Simulating the immune system. Computing in Science and Engineering, 2, 67–77.

    Google Scholar 

  • Kohler, B., Puzone, R., Seiden, P., & Celada, F. (2000). A systematic approach to vaccine complexity using an automaton model of the cellular and humoral immune system, I. Viral characteristics and polarized responses. Vaccine, 19(7–8), 862–876.

    Article  Google Scholar 

  • Langman, R. E., & Cohn, M. (1986). The ‘complete’ idiotype network is an absurd immune system. Immunology Today, 7(4), 100–101.

    Article  Google Scholar 

  • Levi, P., & Kernbach, S. (Eds.) (2010). Symbiotic multi-robot organisms: Reliability, adaptability and evolution. Berlin: Springer.

    MATH  Google Scholar 

  • Lumer, E., & Faieta, B. (1994). Diversity and adaptation in populations of clustering ants. In Proceedings of the third international conference on simulation of adaptive behavior: From animals to animats 3 (pp. 501–508). Cambridge: MIT Press.

    Google Scholar 

  • McEwan, C., & Hart, E. (2009). Representation in the (artificial) immune system. Journal of Mathematical Modelling and Algorithms, 8, 125–149.

    Article  MATH  MathSciNet  Google Scholar 

  • Millonas, M. (1994). Swarms, phase transitions, and collective intelligence. In C. G. Langton (Ed.), Artificial life III (pp. 417–445). Redwood City: Addison-Wesley.

    Google Scholar 

  • Mokhtar, M., Timmis, J., Tyrrell, A., & Bi, R. (2009). A modified dendritic cell algorithm for on-line error detection in robotic system. In IEEE congress on evolutionary computation, 2009, CEC 2009 (pp. 2055–2062). Piscataway: IEEE Press.

    Chapter  Google Scholar 

  • Nanas, N., Uren, V., & de Roeck, A. (2004). Nootropia: A user profiling model based on a self-organising term network. In G. Nicosia, V. Cutello, P. J. Bentley, & J. Timmis (Eds.), Lecture notes in computer science : Vol. 3239. Artificial immune systems, third international conference, ICARIS 2004 (pp. 146–160). Berlin: Springer.

    Chapter  Google Scholar 

  • Newborough, R., & Stepney, S. (2005). A generic framework for population based algorithms. In H. Bersini & J. Carneiro (Eds.), Lecture notes in computer science : Vol. 4163. Artificial immune systems, 5th international conference, ICARIS 2006 (pp. 43–55). Berlin: Springer.

    Chapter  Google Scholar 

  • Orosz, M. (2001). An introduction to immuno-ecology and immuno-informatics. In L. A. Segal & I. R. Cohen (Eds.), Design principles from the immune system (pp. 125–150). New York: Oxford University Press.

    Google Scholar 

  • Owens, N., Timmis, J., Tyrrell, A., & Greensted, A. (2008). Modelling the tunability of early T-cell activation events. In P. J. Bentley, D. Lee, & S. Jung (Eds.), Lecture notes in computer science : Vol. 5132. Artificial immune systems, 7th international conference, ICARIS 2008. Berlin: Springer.

    Chapter  Google Scholar 

  • Owens, N., Greensted, A., Timmis, J., & Tyrrell, A. (2009). T cell receptor signalling inspired kernel density estimation and anomaly detection. In P. S. Andrews, J. Timmis, N. Owens, U. Aickelin, E. Hart, A. Hone, & A. Tyrrell (Eds.), Lecture notes in computer science : Vol. 5666. Artificial immune systems, 8th international conference, ICARIS 2009 (pp. 122–155). Berlin: Springer.

    Chapter  Google Scholar 

  • Perelson, A. S., & Oster, G. F. (1979). Theoretical studies of clonal selection: Minimal antibody repertoire size and reliability of self–non-self discrimination. Journal of Theoretical Biology, 81(4), 645–670.

    Article  MathSciNet  Google Scholar 

  • Read, M., Timmis, J., Andrews, P. S., & Kumar, V. (2009). A domain model of experimental autoimmune encephalomyelitis. In CoSMoS 2009, proceedings of the 2009 international workshop on complex systems modelling and simulation (pp. 3–39). Frome: Luniver Press.

    Google Scholar 

  • Salazar-Bañuelos, A. (2009). Non-deterministic explanation of immune responses: A computer model. In P. S. Andrews, J. Timmis, N. Owens, U. Aickelin, E. Hart, A. Hone, & A. Tyrrell (Eds.), Lecture notes in computer science : Vol. 5666. Artificial immune systems, 8th international conference, ICARIS 2009 (pp. 7–10). Berlin: Springer.

    Chapter  Google Scholar 

  • Segal, L., & Cohen, I. (Eds.) (2001). Design principles for the immune system and other distributed systems. New York: Oxford University Press.

    Google Scholar 

  • Sempo, G., Depickere, S., Ame, J. M., Detrain, C., Halloy, J., & Deneubourg, J. (2006). Integration of an autonomous artificial agent in an insect society: Experimental validation. In Lecture notes in artificial intelligence : Vol. 4095. From animats to animals 9, the ninth international conference on the simulation of adaptive behavior, SAB 2006 (pp. 703–712). Berlin: Springer.

    Google Scholar 

  • Stepney, S., Smith, R., Timmis, J., Tyrrell, A., Neal, M., & Hone, A. (2006). Conceptual frameworks for artificial immune systems. International Journal of Unconventional Computing, 1(3), 315–338.

    Google Scholar 

  • Stibor, T., Timmis, J., & Eckert, C. (2006). On the use of hyperspheres in artificial immune systems as antibody recognition regions. In Lecture notes in computer science : Vol. 4163. Proceedings of 5th international conference on artificial immune systems (ICARIS) (pp. 215–228). Berlin: Springer.

    Google Scholar 

  • SwarmWiki (ongoing). Swarm wiki. http://swarm.org.

  • Timmis, J. (2007). Artificial immune systems: Today and tomorow. Natural Computing, 6(1), 1–18.

    Article  MATH  MathSciNet  Google Scholar 

  • Timmis, J., & Neal, M. (2001). A resource limited artificial immune system for data analysis. Knowledge Based Systems, 14(3–4), 121–130.

    Article  Google Scholar 

  • Timmis, J., Andrews, P. S., Owens, N., & Clark, E. (2008a). An interdisciplinary perspective on artificial immune systems. Evolutionary Intelligence, 1(1), 5–26.

    Article  Google Scholar 

  • Timmis, J., Hart, E., Hone, A., Neal, M., Robins, A., Stepney, S., & Tyrrell, A. (2008b). Immuno-engineering. In IFIP international federation for information processing : Vol. 268. 2nd IFIP international conference on biologically inspired collaborative computing, 20th IFIP world computer congress (pp. 3–17). Berlin: Springer.

    Google Scholar 

  • Timmis, J., Hone, A., Stibor, T., & Clark, E. (2008c). Theoretical advances in artificial immune systems. Journal of Theoretical Computer Science, 403(1), 11–32.

    Article  MATH  MathSciNet  Google Scholar 

  • Timmis, J., Tyrrell, A., Mokhtar, M., Ismail, A., Owens, N., & Bi, R. (2010). An artificial immune system for robot organisms. In P. Levi & S. Kernbach (Eds.), Symbiotic multi-robot organisms: Reliability, adaptability and evolution (pp. 268–288). Berlin: Springer.

    Google Scholar 

  • Warrender, C. (2004). Modeling intercellular interactions in the peripheral immune system. PhD thesis, Computer Science Department, University of New Mexico, NM.

  • Warrender, C., Forrest, S., & Segel, L. (2004). Homeostasis of peripheral immune effectors. Bulletin of Mathematical Biology, 66, 1493–1514.

    Article  Google Scholar 

  • Winfield, A. F., Harper, C. J., & Nembrini, J. (2006). Towards the application of swarm intelligence in safety critical systems. In Proceeding of the 1st IET international conference on system safety (pp. 89–95). Hertfordshire: IEE/IET, Stevenage.

    Chapter  Google Scholar 

  • Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computing, 4, 67–82.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Department of Electronics, University of York, Helsington, York, UK

    Jon Timmis

  2. Department of Computer Science, University of York, Helsington, York, UK

    Paul Andrews

  3. School of Computing, Edinburgh Napier University, Merchiston Campus, Edinburgh, UK

    Emma Hart

Authors
  1. Jon Timmis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paul Andrews
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Emma Hart
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jon Timmis.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Timmis, J., Andrews, P. & Hart, E. On artificial immune systems and swarm intelligence. Swarm Intell 4, 247–273 (2010). https://doi.org/10.1007/s11721-010-0045-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11721-010-0045-5

Keywords

Search

Navigation