Skip to main content
SpringerLink
Log in

Swarm Intelligence

  • Chapter
  • First Online:
Search Methodologies

Abstract

The complex and often coordinated behavior of swarms fascinates not only biologists but also computer scientists. Bird flocking and fish schooling are impressive examples of coordinated behavior that emerges without central control. Social insect colonies show complex problem-solving skills arising from the actions and interactions of nonsophisticated individuals.

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 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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

  • Alrashidi MR, El-Hawary ME (2009) A survey of particle swarm optimization applications in electric power systems. IEEE Trans Evol Comput 13:913–918

    Article  Google Scholar 

  • Banks A, Vincent J, Anyakoha C (2007) A review of particle swarm optimization. Part I: background and development. Nat Comput 6:467–484

    Article  Google Scholar 

  • Banks A, Vincent J, Anyakoha C (2008) A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications. Nat Comput 7:109–124

    Article  Google Scholar 

  • Bauer A, Bullnheimer B, Hartl RF, Strauss C (1999) An ant colony optimization approach for the single machine total tardiness problem. In: Proceedings of the CEC 1999, Washington, DC. IEEE, Piscataway, pp 1445–1450

    Google Scholar 

  • Blackwell TM, Bentley PJ (2002) Dynamic search with charged swarms. In: GECCO 2002, New York. Morgan Kaufmann, San Mateo, pp 19–26

    Google Scholar 

  • Blesa MJ, Blum C, Di Gaspero L, Roli A, Sampels M, Schaerf A (eds) (2009) In: 6th international workshop hybrid metaheuristics, Udine. LNCS 5818. Springer, Berlin

    Google Scholar 

  • Blum C (2005) Ant colony optimization: introduction and recent trends. Phys Life Rev 2:353–373

    Article  Google Scholar 

  • Blum C, Sampels M (2002a) Ant colony optimization for FOP shop scheduling: a case study on different pheromone representations. In: Proceedings of the CEC 2002, Honolulu, pp 1558–1563

    Google Scholar 

  • Blum C, Sampels M (2002b) When model bias is stronger than selection pressure. In: Proceedings of the PPSN VII, Granada. LNCS 2439. Springer, Berlin, pp 893–902

    Google Scholar 

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

    Google Scholar 

  • Brits R, Engelbrecht AP, van den Bergh F (2002) A niching particle swarm optimizer. In: Proceedings of the SEAL 2002, Singapore, pp 692–696

    Google Scholar 

  • Bullnheimer B, Hartl RF, Strauss CA (1999) New rank based version of the ant system: a computational study. Cent Eur J Oper Res Econ 7:25–38

    Google Scholar 

  • Christensen A, O’Grady R, Dorigo M (2009) From fireflies to fault tolerant swarms of robots. IEEE Trans Evol Comput 13:754–766

    Article  Google Scholar 

  • Clerc M (1999) The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. In: Proceedings of the CEC, Washington, DC. IEEE, Piscataway, pp 1951–1957

    Google Scholar 

  • Clerc M (2002) Think locally, act locally—a framework for adaptive particle swarm optimizers. IEEE J Evol Comput 3:1951–1957

    Google Scholar 

  • Clerc M, Kennedy J (2002) The particle swarm—explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6:58–73

    Article  Google Scholar 

  • Cordón O, Fernandez I, Herrera F, Moreno L (2000) A new ACO model integrating evolutionary computation concepts: the best-worst ant system. In: Proceedings of the 2nd international workshop on ant algorithms, Brussels, pp 22–29

    Google Scholar 

  • del Valle Y, Venayagamoorthy GK, Mohagheghi S, Hernandez J-C, Harley RG (2008) Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans Evol Comput 12:171–195

    Article  Google Scholar 

  • Deneubourg J-L, Aron S, Goss S, Pasteels JM (1990) The self-organizing exploratory pattern of the Argentine ant. J Insect Behav 32:159–168

    Article  Google Scholar 

  • Diwold K, Beekman M, Middendorf M (2011) Honeybee optimisation. In: Panigrahi BK et al (eds) Handbook of swarm intelligence—concepts, principles and application. Springer, Berlin, pp 295–328

    Chapter  Google Scholar 

  • Dorigo M (1992) Optimization, learning and natural algorithms (in Italian). PhD thesis, Dipartimento di Elettronica, Politecnico di Milano

    Google Scholar 

  • Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344:243–278

    Article  Google Scholar 

  • Dorigo M, Di Caro G (1999) The ant colony optimization meta-heuristic. In: Corne D et al (eds) New ideas in optimization. McGraw-Hill, New York, pp 11–32

    Google Scholar 

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

    Article  Google Scholar 

  • Dorigo M, Maniezzo V, Colorni A (1991) Positive feedback as a search strategy. Technical report 91-016, Politecnico di Milano

    Google Scholar 

  • Dorigo M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B 26:29–41

    Article  Google Scholar 

  • Dorigo M, Birattari M, Blum C, Clerc M, Stützle T, Winfield AFT (eds) (2008) In: Proceedings of the ANTS 2008, Brussels. LNCS 5217. Springer, Berlin

    Google Scholar 

  • Gambardella LM, Taillard E, Dorigo M (1999) Ant colonies for the quadratic assignment problem. J Oper Res Soc 50:167–176

    Google Scholar 

  • Goss S, Aron S, Deneubourg JL, Pasteels JM (1989) Self-organized shortcuts in the Argentine ant. Naturwissenschaften 76:579–581

    Article  Google Scholar 

  • Guntsch M, Middendorf M (2002a) Applying population based ACO to dynamic optimization problems. In: Proceedings of the 3rd international workshop ANTS 2002, Brussels. LNCS 2463. Springer, Berlin, pp 111–122

    Google Scholar 

  • Guntsch M, Middendorf M (2002b) A population based approach for ACO. In: Proceedings of the EvoWorkshops 2002 on applications of evolutionary computing, Kinsale. LNCS 2279. Springer, Berlin, pp 72–81

    Google Scholar 

  • Gutjahr WJ (2011) Ant colony optimization: recent developments in theoretical analysis. In: Auger A, Doerr B (eds) Theory of randomized search heuristics. World Scientific, Singapore, pp 225–254

    Chapter  Google Scholar 

  • Handl J, Meyer B (2002) Improved ant-based clustering and sorting in a document retrieval interface. In: Merelo Guervos JJ et al (eds) Proceedings of the PPSN VII, Granada. LNCS 2439. Springer, Berlin, pp 913–923

    Google Scholar 

  • OR-Library (2012). http://mscmga.ms.ic.ac.uk/jeb/orlib/wtinfo.html

  • Janson S, Middendorf M (2005) A hierarchical particle swarm optimizer and its adaptive variant. IEEE Syst Man Cybern B 32:1272–1282

    Article  Google Scholar 

  • Karaboga D, Akay B (2009) A survey: algorithms simulating bee swarm intelligence. Artif Intell Rev 31:61–85

    Article  Google Scholar 

  • Kawamura H, Yamamoto M, Suzuki K, Ohucke A (2000) Multiple ant colonies algorithm based on colony level interactions. IEICE Trans Fundam 83A:371–379

    Google Scholar 

  • Kennedy J (1997) The particle swarm: social adaptation of knowledge. In: Proceedings of the CEC, Indianapolis, pp 303–308

    Google Scholar 

  • Kennedy J (2000) Stereotyping: improving particle swarm performance with cluster analysis. In: Proceedings of the CEC, La Jolla, pp 1507–1512

    Google Scholar 

  • Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, Perth, pp 1942–1948

    Google Scholar 

  • Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. Proc Conf Syst Man Cybern 5:4104–4109. IEEE, Piscataway

    Google Scholar 

  • Kennedy J, Eberhart RC (1999) The particle swarm: social adaption in information processing systems. In: Corne D et al (eds) New ideas in optimization. McGraw-Hill, New York, pp 379–387

    Google Scholar 

  • Kennedy J, Mendes R (2003) Neighborhood topologies in fully-informed and best-of-neighborhood particle swarms. In: Proceedings of the IEEE international workshop on soft computing in industrial applications, New York

    Google Scholar 

  • Kennedy J, Shi Y (eds) (2009) In: Proceedings of the 2009 IEEE Swarm Intelligence Symposium, Nashville, IEEE

    Google Scholar 

  • Kennedy J, Eberhart RC, Shi Y (2001) Swarm intelligence. Morgan Kaufmann, San Francisco

    Google Scholar 

  • Ko P-C, Lin P-C (2004) A hybrid swarm intelligence based mechanism for earning forecast. In: Proceedings of the ICITA 2004, Harbin

    Google Scholar 

  • Krink T, Vesterstrøm JS, Riget J (2002) Particle swarm optimisation with spatial particle extension. In: Proceedings of the CEC 2002, Honolulu, pp 1474–1479

    Google Scholar 

  • Labella TH, Dorigo M, Deneubourg J-L (2006) Division of labour in a group of robots inspired by ants’ foraging behaviour. ACM Trans Auton Adapt Syst 1:4–25

    Article  Google Scholar 

  • Lumer ED, Faieta B (1994) Diversity and adaptation in populations of clustering ants. In: Proceedings of the SAB 1994, Brighton. MIT, Cambridge, pp 501–508

    Google Scholar 

  • Maniezzo V (1999) Exact and approximate nondeterministic tree-search procedures for the quadratic assignment problem. Inf J Comput 11:358–369

    Article  Google Scholar 

  • Merkle D, Middendorf M (2002) Ant colony optimization with the relative pheromone evaluation method. In: Proceedings of the EvoWorkshops 2001, Como. LNCS 2279. Springer, Berlin, pp 325–333

    Google Scholar 

  • Merkle D, Middendorf M (2003a) On the behavior of ACO algorithms: studies on simple problems. In: Resende MGC, Pinho de Sousa J (eds) Metaheuristics: computer decision-making. Kluwer, Dordrecht, pp 465–480

    Google Scholar 

  • Merkle D, Middendorf M (2003b) An ant algorithm with global pheromone evaluation for scheduling a single machine. Appl Intell 18:105–111

    Article  Google Scholar 

  • Merkle D, Middendorf M (2005) On solving permutation scheduling problems with ant colony optimization. Int J Syst Sci 36:255–266

    Article  Google Scholar 

  • Merkle D, Middendorf M (2008) Swarm intelligence and signal processing. IEEE Signal Process Mag 25:152–158

    Article  Google Scholar 

  • Merkle D, Middendorf M, Schmeck H (2002) Ant colony optimization for resource-constrained project scheduling. IEEE Trans Evol Comput 6:333–346

    Article  Google Scholar 

  • Michels R, Middendorf M (1999) An ant system for the shortest common supersequence problem. In: Corne D, Dorigo M, Glover F (eds) New ideas in optimization. McGraw-Hill, New York, pp 51–61

    Google Scholar 

  • Montgomery J, Randall M (2002) Anti-pheromone as a tool for better exploration of search space. In: Proceedings of the ANTS 2002, Brussels. LNCS 2463. Springer, Berlin, pp 100–110

    Google Scholar 

  • Oliveira SM, Hussin MS, Stützle T, Roli A, Dorigo M (2011) A detailed analysis of the population-based ant colony optimization algorithm for the TSP and the QAP. In: GECCO (Companion), Dublin, pp 13–14

    Google Scholar 

  • Parsopoulos KE, Vrahatis MN (2001) Modification of the particle swarm optimizer for locating all the global minima. In: Kurkova V et al (eds) Artificial neural networks and genetic algorithms. Springer, Berlin, pp 324–327

    Chapter  Google Scholar 

  • Parsopoulos KE, Tasoulis DK, Vrahatis MN (2004) Multiobjective optimization using parallel vector evaluated particle swarm optimization. In: Proceedings of the IASTED international conference on artificial intelligence and applications, Innsbruck

    Google Scholar 

  • Pedersen MEH, Chipperfield AJ (2010) Simplifying particle swarm optimization. Appl Soft Comput 10:618–628

    Article  Google Scholar 

  • Poli R (2008) Analysis of the publications on the applications of particle swarm optimisation. J Artif Evol Appl 1:1–10

    Article  Google Scholar 

  • Riget J, Vesterstrøm JS (2002) A diversity-guided particle swarm optimizer—the ARPSO. Technical report no 2002-02, University of Aarhus

    Google Scholar 

  • Ritscher T, Helwig S, Wanka R (2010) Design and experimental evaluation of multiple adaptation layers in self-optimizing particle swarm optimization. In: Proceedings of the CEC 2010, Barcelona, pp 1–8

    Google Scholar 

  • Sedighizadeh D, Masehian E (2009) Particle swarm optimization methods, taxonomy and applications. Int J Comput Theor Eng 1:1793–8201

    Google Scholar 

  • Stützle T, Hoos H (1997) Improvements on the ant system: introducing MAX(MIN) ant system. In: Proceedings of the international conference on artificial neutral networks and genetic algorithms. Springer, Berlin, pp 245–249

    Google Scholar 

  • Stützle T, Hoos H (2000) MAX-MIN ant system. Future Gener Comput Syst J 16:889–914

    Article  Google Scholar 

  • Stützle T, den Besten M, Dorigo M (2000) Ant colony optimization for the total weighted tardiness problem. In: Deb et al (eds) Proceedings of the PPSN-VI, Paris. LNCS 1917. Springer, Berlin, pp 611–620

    Google Scholar 

  • Sumpter DJT (2009) Collective animal behavior. Princeton University Press, Princeton

    Google Scholar 

  • Sun J, Feng B, Xu W (2004) Particle swarm optimization with particles having quantum behavior. In: IEEE Proceeding of the CEC, San Diego, pp 325–331

    Google Scholar 

  • van den Bergh F, Engelbrecht AP (2000) Cooperative learning in neural networks using particle swarm optimizers. S Afr Comput J 26:84–90

    Google Scholar 

  • Vesterstrøm JS, Riget J, Krink T (2002) Division of labor in particle swarm optimisation. In: Proceedings of the CEC 2002, Honolulu, pp 1570–1575

    Google Scholar 

  • Xie X-F, Zhang W-J, Yang Z-L (2002) A dissipative particle swarm optimization. In: Proceedings of the CEC 2002, Honolulu

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of South Denmark, Odense, Denmark

    Daniel Merkle

  2. Department of Computer Science, University of Leipzig, Leipzig, Germany

    Martin Middendorf

Authors
  1. Daniel Merkle
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Martin Middendorf
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Martin Middendorf .

Editor information

Editors and Affiliations

  1. Computing Science and Mathematics, University of Stirling, Scotland, United Kingdom

    Edmund K. Burke

  2. Research and Knowledge Transfer, University of Nottingham, Malaysia & UK, Semenyih, Malaysia

    Graham Kendall

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this chapter

Cite this chapter

Merkle, D., Middendorf, M. (2014). Swarm Intelligence. In: Burke, E., Kendall, G. (eds) Search Methodologies. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-6940-7_8

Download citation

Publish with us

Policies and ethics

Search

Navigation