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Swarm Intelligence

  • Chapter
Introduction to Evolutionary Algorithms

Part of the book series: Decision Engineering ((DECENGIN,volume 0))

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Abstract

We look at the natural selection process as a learning or optimizing process and apply the survival of the fittest principle to designing the learning and optimizing algorithm. Then many EAs, e.g., GA, ES, EP, DE, etc., are suggested accordingly. There are other similar phenomena in nature. A swarm of “low-level” (not smart) insects sometimes surprises us with their amazing behaviors, such as foraging for food efficiently and constructing exquisite nests. We can also look at the process of foraging for food and constructing nest as a learning or optimizing process and learn to design corresponding algorithms. This swarm-level smart behavior generated by an agent-level, not smart property could enlighten us to suggest more robust algorithms for more complex problems in an uncertain environment.

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References

  1. Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B Cybern 26(1):29–41

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Watkins C (1989) Learning from delayed rewards. Ph.D. thesis, University of Cambridge, UK

    Google Scholar 

  4. Kaelbling LP, Littman M, Moore A (1996) Reinforcement learning: a survey. J Artif Intell Res 4:237–285

    Google Scholar 

  5. Bullnheimer B, Hartl RF, Strauß C (1999) A new rank based version of the ant system - a computational study. Central Eur J Oper Res and Econ 7:25–38

    MATH  Google Scholar 

  6. Stützle T, Hoos HH (2000) MAX-MIN ant system. Future Gener Comput Syst 16(8):889–914

    Article  Google Scholar 

  7. Blum C, Dorigo M (2004) The hyper-cube framework for ant colony optimization. IEEE Trans Syst Man Cybern B Cybern 34(2):1161–1172

    Article  Google Scholar 

  8. Birattari M, Pellegrini P, Dorigo M (2007) On the invariance of ant colony optimization. IEEE Trans Evol Comput 11(6):732–742

    Article  Google Scholar 

  9. Socha K, Dorigo M (2008) Ant colony optimization for continuous domains. Eur J Oper Res 185(3):1155–1173

    Article  MATH  MathSciNet  Google Scholar 

  10. Kern S, Müller SD, Hansen N et al (2004) Learning probability distributions in continuous evolutionary algorithms - a comparative review. Nat Comput 3(3):355–356

    Article  Google Scholar 

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

    Google Scholar 

  12. Shi Y, Eberhart R (1999) Empirical study of particle swarm optimization. In: Proceedings of the IEEE congress on evolutionary computation, pp 1945–1950

    Google Scholar 

  13. Eberhart R, Shi Y (2001) Tracking and optimizing dynamic systems with particle swarms. In: Proceedings of the IEEE congress on evolutionary computation, pp 94–100

    Google Scholar 

  14. Eberhart RC, Dobbins R, Simpson PK (1996) Computational intelligence PC tools. Morgan Kaufmann, San Francisco

    Google Scholar 

  15. Liang J, Qin A, Suganthan P et al (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295

    Article  Google Scholar 

  16. Kennedy J (1999) Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Proceedings of the IEEE congress on evolutionary computation, pp 1931–1938

    Google Scholar 

  17. Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: Proceedings of the IEEE congress on evolutionary computation, pp 1671–1676

    Google Scholar 

  18. Clerc M, Kennedy J (2002) The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73

    Article  Google Scholar 

  19. Eberhart R, Shi Y (2000) Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the IEEE congress on evolutionary computation, pp 84–88

    Google Scholar 

  20. Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput 8(3):204–210

    Article  Google Scholar 

  21. Kennedy J, Mendes R (2006) Neighborhood topologies in fully informed and best-ofneighborhood particle swarms. IEEE Trans Syst Man Cybern C Appl Rev 36(4):515–519

    Article  Google Scholar 

  22. Kennedy J (2003) Bare bones particle swarms. In: Proceedings of the IEEE swarm intelligence symposium, pp 80–87

    Google Scholar 

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

    MATH  Google Scholar 

  24. Weiss G (2000) Multiagent systems: a modern approach to distributed artificial intelligence. MIT Press, Cambridge, MA

    Google Scholar 

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

    Google Scholar 

  26. Engelbrecht AP (2006) Fundamentals of computational swarm intelligence. Wiley, New York

    Google Scholar 

  27. Abraham A, Grosan C, Ramos V (2006) Swarm intelligence in data mining. Springer, Berlin Heidelberg New York

    Book  MATH  Google Scholar 

  28. Blum C, Merkle D (2008) Swarm intelligence: introduction and applications. Springer, Berlin Heidelberg New York

    Book  MATH  Google Scholar 

  29. Dorigo M, Stützle T (2004) Ant colony optimization. MIT Press, Cambridge, MA

    MATH  Google Scholar 

  30. Solnon C (2010) Ant colony optimization and constraint programming. Wiley-ISTE, New York

    Google Scholar 

  31. Dorigo M, Caro GD, Gambardella LM (1999) Ant algorithms for discrete optimization. Artif Life 5(2):137–172

    Article  Google Scholar 

  32. Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39

    Google Scholar 

  33. Dorigo M, Bonabeau E, Theraulaz G (2000) Ant algorithms and stigmergy. Future Gener Comput Syst 16(9):851–871

    Article  Google Scholar 

  34. Zecchin A, Simpson A, Maier H et al (2005) Parametric study for an ant algorithm applied to water distribution system optimization. IEEE Trans Evol Comput 9(2):175–191

    Article  Google Scholar 

  35. Blum C, Dorigo M (2005) Search bias in ant colony optimization: on the role of competitionbalanced systems. IEEE Trans Evol Comput 9(2):159–174

    Article  MathSciNet  Google Scholar 

  36. Solnon C (2002) Ants can solve constraint satisfaction problems. IEEE Trans Evol Comput 6(4):347–357

    Article  Google Scholar 

  37. Leguizamón G, Coello C (2009) Boundary search for constrained numerical optimization problems with an algorithm inspired by the ant colony metaphor. IEEE Trans Evol Comput 13(2):350–368

    Article  Google Scholar 

  38. Merkle D, Middendorf M, Schmeck H (2002) Ant colony optimization for resourceconstrained project scheduling. IEEE Trans Evol Comput 6(4):333–346

    Article  Google Scholar 

  39. Martens D, Backer MD, Haesen R et al (2007) Classification with ant colony optimization. IEEE Trans Evol Comput 11(5):651–665

    Article  Google Scholar 

  40. Nezamabadi-pour H, Saryazdi S, Rashedi E (2006) Edge detection using ant algorithms. Soft Comput 10(7):623–628

    Article  Google Scholar 

  41. Lim KK, Ong Y, Lim MH et al (2008) Hybrid ant colony algorithms for path planning in sparse graphs. Soft Comput 12(10):981–994

    Article  Google Scholar 

  42. Clerc M (2006) Particle swarm optimization. ISTE Publishing Company, London, UK

    Book  MATH  Google Scholar 

  43. Poli R, Kennedy J, Blackwell T et al (2008) Particle swarms: the second decade. J Artif Evol Appl 2008:1–3

    Google Scholar 

  44. Mikki S, Kishk A (2008) Particle swarm optimizaton: a physics-based approach. Morgan and Claypool, San Rafael, CA

    Google Scholar 

  45. Parsopoulos KE, Vrahatis MN (2009) Particle swarm optimization and intelligence: advances and applications. Information Science, LinkHershey, PA

    Google Scholar 

  46. Banks A, Vincent J, Anyakoha C (2007) A review of particle swarm optimization. I: background and development. Nat Comput 6(4):467–484

    Article  MATH  MathSciNet  Google Scholar 

  47. Banks A, Vincent J, Anyakoha C (2008) A review of particle swarm optimization. II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications. Nat Comput 7(1):109–124

    Article  MATH  MathSciNet  Google Scholar 

  48. Ratnaweera A, Halgamuge S, Watson H (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240–255

    Article  Google Scholar 

  49. van den Bergh F, Engelbrecht A (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evol Comput 8(3):225–239

    Article  Google Scholar 

  50. Kadirkamanathan V, Selvarajah K, Fleming P (2006) Stability analysis of the particle dynamics in particle swarm optimizer. IEEE Trans Evol Comput 10(3):245–255

    Article  Google Scholar 

  51. Blackwell T, Branke J (2006) Multiswarms, exclusion, and anti-convergence in dynamic environments. IEEE Trans Evol Comput 10(4):459–472

    Article  Google Scholar 

  52. Janson S, Middendorf M (2006) A hierarchical particle swarm optimizer for noisy and dynamic environments. Genet Programm Evolvable Mach 7(4):329–354

    Article  Google Scholar 

  53. Langdon W, Poli R (2007) Evolving problems to learn about particle swarm optimizers and other search algorithms. IEEE Trans Evol Comput 11(5):561–578

    Article  Google Scholar 

  54. Vrugt J, Robinson B, Hyman J (2009) Self-adaptive multimethod search for global optimization in Real-Parameter spaces. IEEE Trans Evol Comput 13(2):243–259

    Article  Google Scholar 

  55. Liang J, Suganthan P (2006) Dynamic multi-swarm particle swarm optimizer with a novel constraint-handling mechanism. In: Proceedings of the IEEE congress on evolutionary computation, pp 9–16

    Google Scholar 

  56. Parsopoulos K, Vrahatis M (2004) On the computation of all global minimizers through particle swarm optimization. IEEE Trans Evol Comput 8(3):211–224

    Article  MathSciNet  Google Scholar 

  57. Parrott D, Li X (2006) Locating and tracking multiple dynamic optima by a particle swarm model using speciation. IEEE Trans Evol Comput 10(4):440–458

    Article  Google Scholar 

  58. Coello C, Pulido G, Lechuga M (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 8(3):256–279

    Article  Google Scholar 

  59. Ho S, Ku W, Jou J et al (2006) Intelligent particle swarm optimization in multi-objective problems, In: Ng WK, Kitsuregawa M, Li J et al (eds) Advances in knowledge discovery and data mining. Springer, Berlin Heidelberg New York, pp 790–800

    Chapter  Google Scholar 

  60. Reyes-Sierra M, Coello Coello CA (2006) Multi-objective particle swarm optimizers: A survey of the-state-of-the-art. Tech. rep., CINVESTAV-IPN, Mexico

    Google Scholar 

  61. Hastings E, Guha R, Stanley K (2009) Interactive evolution of particle systems for computer graphics and animation. IEEE Trans Evol Comput 13(2):418–432

    Article  Google Scholar 

  62. O’Neill M, Brabazon A (2008) Self-organising swarm (SOSwarm). Soft Comput 12:1073–1080

    Article  Google Scholar 

  63. del Valle Y, Venayagamoorthy G, Mohagheghi S et al (2008) Particle swarm optimization: Basic concepts, variants and applications in power systems. IEEE Trans Evol Comput 12(2):171–195

    Article  Google Scholar 

  64. Li S, Wu X, Tan M (2008) Gene selection using hybrid particle swarm optimization and genetic algorithm. Soft Comput 12(11):1039–1048

    Article  Google Scholar 

  65. Rahimi-Vahed AR, Mirghorbani SM, Rabbani M (2007) A new particle swarm algorithm for a multi-objective mixed-model assembly line sequencing problem. Soft Comput 11(10):997–1012

    Article  Google Scholar 

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© 2010 Springer-Verlag London Limited

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(2010). Swarm Intelligence. In: Introduction to Evolutionary Algorithms. Decision Engineering, vol 0. Springer, London. https://doi.org/10.1007/978-1-84996-129-5_8

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  • DOI: https://doi.org/10.1007/978-1-84996-129-5_8

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84996-128-8

  • Online ISBN: 978-1-84996-129-5

  • eBook Packages: EngineeringEngineering (R0)

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