Skip to main content
SpringerLink
Log in

Improved Adaptive Differential Evolution Algorithm with External Archive

  • Conference paper
Swarm, Evolutionary, and Memetic Computing (SEMCCO 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8297))

Included in the following conference series:

Abstract

Depending on the complexity of the optimization problem, the performance of differential evolution (DE) algorithm is quite sensitive to the choice of mutation and crossover strategies and their associated control parameters. To obtain optimal performance, while avoiding time consuming parameter tuning, different adaptive and self-adaptive techniques that can update the strategies and/or the parameters during the evolution have been proposed. Adaptive differential evolution with optional archive (JADE) is one of the popular adaptive algorithms that perform well on most of the optimization problems. Motivated by the performance of the JADE algorithm, this paper presents an improved adaptive differential evolution algorithm with external archive (iJADE). Unlike the optional archive in JADE, iJADE algorithm employs an external archive which is updated every generation by tournament selection to incorporate the parents which cannot progress to the next generation. In addition, iJADE uses an ensemble of two crossover strategies, binomial and exponential, instead of a single crossover strategy as in JADE. The performance of the algorithm is evaluated on a set of 16 bound-constrained problems designed for Conference on Evolutionary Computation (CEC) 2005 and is compared with JADE algorithm.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Storn, R., Price, K.: Differential Evolution - A Simple and Efficient Adaptive Scheme for Global Optimization Over Continuous Spaces. Technical Report TR-95-012, ICSI (1995), http://http.icsi.berkeley.edu/~storn/litera.html

  2. Joshi, R., Sanderson, A.C.: Minimal representation multisensor fusion using differential evolution. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans 29(1), 63–76 (1999)

    Article  Google Scholar 

  3. Mallipeddi, R., et al.: Robust Adaptive Beamforming Based on Covariance Matrix Reconstruction for Look Direction Mismatch. Progress in Electromagnetic Research Letters 25, 37–46 (2011)

    Google Scholar 

  4. Venu, M.K., Mallipeddi, R., Suganthan, P.N.: Fiber Bragg grating sensor array interrogation using differential evolution. Optoelectronics and Advanced Materials - Rapid Communications 2(11), 682–685 (2008)

    Google Scholar 

  5. Das, S., Konar, A.: Automatic image pixel clustering with an improved differential evolution. Applied Soft Computing 9(1), 226–236 (2009)

    Article  Google Scholar 

  6. Storn, R.: Differential evolution design of an IIR-filter. In: Storn, R. (ed.) IEEE International Conference on Evolutionary Computation 1996, pp. 268–273. IEEE (1996)

    Google Scholar 

  7. Mallipeddi, R., et al.: Efficient constraint handling for optimal reactive power dispatch problems. Swarm and Evolutionary Computation 5, 28–36 (2012)

    Article  Google Scholar 

  8. Gämperle, R., Müller, S.D., Koumoutsakos, P.: A Parameter Study for Differential Evolution. In: Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation 2002, pp. 293–298. WSEAS Press, Interlaken (2002)

    Google Scholar 

  9. Liu, J., Lampinen, J.: On setting the control parameter of the differential evolution method. In: Proc. 8th Int, Conf. Soft Computing, MENDEL 2002 (2002)

    Google Scholar 

  10. Jingqiao, Z., Sanderson, A.C.: An approximate gaussian model of Differential Evolution with spherical fitness functions. In: IEEE Congress on Evolutionary Computation, CEC 2007 (2007)

    Google Scholar 

  11. Mallipeddi, R., et al.: Differential Evolution Algorithm with Ensemble of Parameters and Mutation Strategies. Applied Soft Computing 11(2), 1679–1696 (2011)

    Article  Google Scholar 

  12. Brest, J., et al.: Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation 10(8), 646–657 (2006)

    Article  Google Scholar 

  13. Omran, M.G.H., Salman, A., Engelbrecht, A.P.: Self-adaptive Differential Evolution. In: Hao, Y., Liu, J., Wang, Y.-P., Cheung, Y.-m., Yin, H., Jiao, L., Ma, J., Jiao, Y.-C. (eds.) CIS 2005. LNCS (LNAI), vol. 3801, pp. 192–199. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  14. Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential Evolution Algorithm with Strategy Adaptation for Global Numerical Optimization. IEEE Transactions on Evolutionary Computation 13(2), 398–417 (2009)

    Article  Google Scholar 

  15. Zaharie, D.: Control of Population Diversity and Adaptation in Differential Evolution Algorithms. In: Proceedings of the 9th International Conference on Soft Computing, Brno, pp. 41–46 (2003)

    Google Scholar 

  16. Tvrdik, J.: Adaptation in differential evolution: A numerical comparison. Applied Soft Computing 9(3), 1149–1155 (2009)

    Article  MathSciNet  Google Scholar 

  17. Zhang, J.: JADE: Adaptive Differential Evolution with Optional External Archive. IEEE Transactions on Evolutionary Computation 13(5), 945–958 (2009)

    Article  Google Scholar 

  18. Storn, R., Price, K.: Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization 11(4), 341–359 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  19. Das, S., Konar, A., Chakraborty, U.K.: Two Improved Differential Evolution Schemes for Faster Global Search. In: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, pp. 991–998 (2005)

    Google Scholar 

  20. Lampinen, J., Zelinka, I.: On Stagnation of the Differential Evolution Algorithm. In: Proceedings of MENDEL 2000, 6th International Mendel Conference on Soft Computing, pp. 76–83 (2000)

    Google Scholar 

  21. Price, K.V., Storn, R.M., Lampinen, J.A. (eds.): Differential Evolution: A Practical Approach to Global Optimization. Natural Computing Series. Springer, Berlin (2005)

    Google Scholar 

  22. Storn, R., Price, K.: Differential Evolution: A Simple Evolution Strategy for Fast Optimization. Dr. Dobb’s Journal 22(4), 18–24 (1997)

    MathSciNet  Google Scholar 

  23. Storn, R.: On the Usage of Differential Evolution for Function Optimization. In: Biennial Conference of the North American Fuzzy Information Processing Society (NAFIPS), pp. 519–523. IEEE, Berkeley (1996)

    Chapter  Google Scholar 

  24. Rönkkönen, J., Kukkonen, S., Price, K.V.: Real-parameter optimization with differential evolution. In: IEEE Congress on Evolutionary Computation (2005)

    Google Scholar 

  25. Abbass, H.A.: The Self-Adaptive Pareto Differential Evolution Algorithm. In: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 1, pp. 831–836 (2002)

    Google Scholar 

  26. Liu, J., Lampinen, J.: A fuzzy adaptive differential evolution algorithm. Soft Computing 9(6), 448–462 (2005)

    Article  MATH  Google Scholar 

  27. Zaharie, D., Petcu, D.: Adaptive Pareto differential evolution and its parallelization. In: Proc. of 5th International Conference on Parallel Processing and Applied Mathematics, Czestochowa, Poland (2003)

    Google Scholar 

  28. Das, S., Suganthan, P.N.: Differential Evolution: A Survey of the State-of-the-art. IEEE Trans. on Evolutionary Computation 15(1), 4–31 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Kyungpook National University, Taegu, South Korea, 702 701

    Rammohan Mallipeddi

  2. Nanyang Technological University, Singapore, 639 798

    Ponnuthurai Nagaratnam Suganthan

Authors
  1. Rammohan Mallipeddi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ponnuthurai Nagaratnam Suganthan
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Electrical Engineering Dept., IIT Delhi, Hauz Khas, 110016, New Delhi, India

    Bijaya Ketan Panigrahi

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, 700108, Kolkata, India

    Swagatam Das

  4. SRM Engineering College, Department of Electrical and Electronics Engineering, SRM University, Potheri, Kattankulathur, Kancheepuram, 603203, Tamil Nadu, India

    Shubhransu Sekhar Dash

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer International Publishing Switzerland

About this paper

Cite this paper

Mallipeddi, R., Suganthan, P.N. (2013). Improved Adaptive Differential Evolution Algorithm with External Archive. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8297. Springer, Cham. https://doi.org/10.1007/978-3-319-03753-0_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-03753-0_16

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03752-3

  • Online ISBN: 978-3-319-03753-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation