Skip to main content
SpringerLink
Log in

Evolutionary Computation

  • Chapter
  • First Online:
Condition Monitoring and Assessment of Power Transformers Using Computational Intelligence

Part of the book series: Power Systems ((POWSYS))

Abstract

The basic approach to optimisation is to formulate a fitness function, which evaluates the performance of the fitness function and improves this performance by choosing from available alternatives. Most classical optimisation methods produce a deterministic sequence of trial solutions using the gradient or higher-order statics of the fitness function. However, such methods may converge to local optimal solutions. The evolutionary computation approach is a population-based optimisation process rooted on the model of organic evolution, which can outperform the classical optimisation methods for many engineering problems. The existing approaches to evolutionary computation include genetic algorithms, evolution strategies, evolutionary programming, genetic programming and so on, which are considerably different in their practical instantiations. The emphasis of this chapter is put on the biological background and basic foundations of genetic algorithm and evolutionary programming. As the principles of particle swarm optimisation are similar to that of evolutionary algorithms, the standard particle swarm optimisation algorithm and an improved particle swarm optimisation algorithm are also presented in this chapter.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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

  1. Goldberg D (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Publishing Company, Inc., USA

    MATH  Google Scholar 

  2. Coello CAC, Veldhuizen DAV, Lamont GB (2002) Evolutionary algorithms for solving multi-objective problems. Kluwer Academic Publishers, New York

    MATH  Google Scholar 

  3. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge

    MATH  Google Scholar 

  4. Fogel LJ (1994) Evolutionary programming in perspective: the top-down view. In: Zurada JM, Marks II RJ, Robinson CJ (eds) Computational intelligence: imitating life. IEEE Press, Piscataway

    Google Scholar 

  5. Rechenberg I (1994) Evolution strategy. In: Zurada JM, Marks II RJ, Robinson C (eds) Computational intelligence: imitating life. IEEE Press, Piscataway

    Google Scholar 

  6. Reeves CR, Rowe JE (2003) Genetic algorithms—principles and perspectives (a guide to GA theory). Kluwer Academic Publishers, London

    Google Scholar 

  7. Miettinen K (1999) Evolutionary algorithms in engineering and computer science. JohnWiley and Sons Inc., UK

    MATH  Google Scholar 

  8. Bäck T (1996) Evolution algorithms in theory and practice. Oxford University Press, NY

    MATH  Google Scholar 

  9. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: IEEE international conference on neural networks, vol 4. IEEE Press, NY, pp 1942–1948

    Google Scholar 

  10. Kennedy J, Eberhart RC (2001) Swarm intelligence. Morgan Kaufmann Publishers, San Francisco

    Google Scholar 

  11. He S, Wu QH, Wen JY, Saunders JR, Paton RC (2004) A particle swarm optimizer with passive congregation. BioSystems 78(1–3):135–147

    Article  Google Scholar 

  12. Holland J (1975) Adaptation in natural and artificial systems. University of Michigan Press, USA

    Google Scholar 

  13. Wu QH, Cao YJ, Wen JY (1998) Optimal reactive power dispatch using an adaptive genetic algorithm. Int J Electr Power Energy Syst 20(8):563–569

    Article  Google Scholar 

  14. Walters DC, Sheble GB (1993) Genetic algorithm solution of economic dispatch with valve point loading. IEEE Trans Power Syst 8(3):1325–1332

    Article  Google Scholar 

  15. Wu QH, Ma JT (1995) Power system optimal reactive dispatch using evolutionary programming. IEEE Trans Power Syst 10(3):1243–1249

    Article  Google Scholar 

  16. Iba K (1994) Reactive power optimization by genetic algorithm. IEEE Trans Power Syst 9(2):685–692

    Article  Google Scholar 

  17. Lee KY, Bai X, Park YM (1995) Optimization method for reactive power planning using a genetic algorithm. IEEE Trans Power Syst 10(4):1843–1850

    Article  Google Scholar 

  18. Ma JT, Wu QH (1995) Generator parameter identification using evolutionary programming. Int J Electr Power Energy Syst 17(6):417–423

    Article  MathSciNet  Google Scholar 

  19. Zhao Y, Edwards RM, Lee KY (1997) Hybrid feedforward and feedback controller design for nuclear steam generators over wide range operation using genetic algorithm. IEEE Trans Energy Conv 12(1):100–106

    Article  Google Scholar 

  20. Michalewicz Z (1994) Genetic algorithms + data structures = evolution programs. AI series. Springer-Verlag, New York

    Google Scholar 

  21. Langdon WB (1998) Genetic programming and data structures: genetic programming + data structures = automatic programming. Kluwer Academic Publishers, Boston

    MATH  Google Scholar 

  22. Banzhaf W, Nordin P, Keller RE, Francone FD (1998) Genetic programming-an introduction: on the automatic evolution of computer program and its applications. Morgan Kaufmann Publishers Inc., San Francisco

    Google Scholar 

  23. Koza JR (1994) Genetic programming: automatic discovery of reusable programs. MIT Press, Cambridge

    MATH  Google Scholar 

  24. Guo H, Jack LB, Nandi AK (2005) Feature generation using genetic programming with application to fault classification. IEEE Trans Syst Man Cybernet B: Cybernet 35(1):89–99

    Article  Google Scholar 

  25. Theodoridis S, Koutroumbas K (2003) Pattern recognition, 2nd edn. Academic Press, London

    Google Scholar 

  26. Zhang L, Jack LB, Nandi AK (2005) Fault detection using genetic programming. Mech Syst Signal Process 19:271–289

    Article  Google Scholar 

  27. Bäck T, Fogel DB, Michalewicz Z (2000) Evolutionary computation 1: basic algorithms and operators. Institute of Physics Publishing Ltd., Bristol

    MATH  Google Scholar 

  28. Angeline P (1998) Evolutionary optimization versus particle swarm optimization: philosophy and performance difference. In: Proceedings of the evolutionary programming conference, San Diago, USA

    Google Scholar 

  29. Løvbjerg M, Rasmussen T, Krink K (2001) Hybrid particle swarm optimizer with breeding and subpopulations. In: Proceedings of the third genetic and evolutionary computation conference (GECCO-2001), vol 1. pp 469–476

    Google Scholar 

  30. Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: Proceedings of the 2002 congress on evolutionary computation CEC2002. IEEE Press, pp 1671–1676

    Google Scholar 

  31. Kennedy J (2000) Stereotyping: improving particle swarm performance with cluster analysis. In: Proceedings of the IEEE international conference on evolutionary computation. pp 1507–1512

    Google Scholar 

  32. Shi Y, Eberhart RC (2001) Fuzzy adaptive particle swarm optimization. In: Proceedings of the IEEE international conference on evolutionary computation. pp 101–106

    Google Scholar 

  33. Yoshida H, Kawata K, Fukuyama Y, Takayama S, Nakanishi Y (2000) A particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Trans Power Syst 15(4):1232–1239

    Article  Google Scholar 

  34. Parrish JK, Hamner WM (1997) Animal groups in three dimensions. Cambridge University Press, Cambridge

    Book  Google Scholar 

  35. Shi Y, Eberhart RC (1998) Parameter selection in particle swarm optimization. Evolutionary programming VII (1998). Lecture notes in computer science, vol 1447. Springer, NY, pp 591–600

    Google Scholar 

  36. Gordon DM, Paul RE, Thorpe K (1993) What is the function of encounter pattern in ant colonies? Anim Behav 45:1083–1100

    Article  Google Scholar 

  37. Hilborn R (1991) Modelling the stability of fish schools: exchange of individual fish between schools of skipjack tuna (Katsuwonus pelamis). Canad J Fish Aqua Sci 48:1080–1091

    Google Scholar 

  38. Hamilton WD (1971) Geometry for the selfish herd. J Theor Biol 31:295–311

    Article  Google Scholar 

  39. Pitcher TJ, Parrish JK (1993) Functions of shoaling behaviour in teleosts. Pitcher TJ (ed) Behaviour of teleost fishes. Chapman and Hall, London, pp 363–439

    Google Scholar 

  40. Magurran AE, Higham A (1988) Information transfer across fish shoals under predator threat. Ethology 78:153–158

    Article  Google Scholar 

  41. Alexander RD (1974) The evolution of social behaviour. Annu Rev Ecol Syst 5:325–383

    Article  Google Scholar 

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

    MATH  Google Scholar 

  43. Deneubourg JL, Goss S, Franks N, Sendova-Franks A, Detrain C, Chretien L (1991) The dynamics of collective sorting: robot-like ant and ant-like robot. In: Proceedings of the first conference on simulation of adaptive behavior: from animals to animals. pp 356–365

    Google Scholar 

  44. Lumer E, Faieta B (1994) Diversity and adaptation in population of clustering ants. In: Proceedings of the third international conference on simulation of adaptive behavior: from animals to animals. pp 499–508

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Electronics, The University of Liverpool, Brownlow Hill, L69 3GJ, Liverpool, UK

    W. H. Tang & Q. H. Wu

Authors
  1. W. H. Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Q. H. Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to W. H. Tang .

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag London Limited

About this chapter

Cite this chapter

Tang, W.H., Wu, Q.H. (2011). Evolutionary Computation. In: Condition Monitoring and Assessment of Power Transformers Using Computational Intelligence. Power Systems. Springer, London. https://doi.org/10.1007/978-0-85729-052-6_2

Download citation

  • DOI: https://doi.org/10.1007/978-0-85729-052-6_2

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-051-9

  • Online ISBN: 978-0-85729-052-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation