Advertisement

Differential Evolution: An Updated Survey

  • Nadeem Javaid
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 772)

Abstract

Optimization is required every where from science and engineering to decision making in business and implementation in industry. The optimization is desired to achieve a solution with minimum cost and maximum reliability of the system based on the decision variables. Moreover, the decision variables operate within the defined threshold to satisfy the requirements of the objective function. In this regard, evolutionary algorithms are widely accepted in finding near optimal solution. In this study, a survey on differential evolution (DE) scheme has been conducted to highlight its ability in solving optimization problems. The characteristics used by DE to solve single objective optimization problems are given in detail to enlighten the adaptable nature of DE. Moreover, an overview of multi objective optimization problem is also presented to show the qualities of DE in finding near optimal solution. Further, the applications of DE are discussed in multi disciplinary fields. Furthermore, in this paper, we provide critical analysis and unfold the potential future challenges against DE.

References

  1. 1.
    Fogel, D.B.: Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, vol. 1. Wiley, New York (2006)zbMATHGoogle Scholar
  2. 2.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing, vol. 53. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  3. 3.
    Neri, F., Tirronen, V.: Recent advances in differential evolution: a survey and experimental analysis. Artif. Intell. Rev. 33(1–2), 61–106 (2010)CrossRefGoogle Scholar
  4. 4.
    Das, S., Mullick, S.S., Suganthan, P.N.: Recent advances in differential evolutionan updated survey. Swarm Evol. Comput. 27, 1–30 (2016)CrossRefGoogle Scholar
  5. 5.
    Dragoi, E.N., Dafinescu, V.: Parameter control and hybridization techniques in differential evolution: a survey. Artif. Intell. Rev. 45(4), 447–470 (2016)CrossRefGoogle Scholar
  6. 6.
    Fan, G.M., Huang, H.J.: A hybrid discrete differential evolution algorithm for stochastic resource allocation. In: 2016 35th Chinese Control Conference (CCC), pp. 2756–2759. IEEE, July 2016Google Scholar
  7. 7.
    Sakr, W.S., El-Sehiemy, R.A., Azmy, A.M.: Optimal allocation of TCSCs by adaptive DE algorithm. IET Gener. Transm. Distrib. 10(15), 3844–3854 (2016)CrossRefGoogle Scholar
  8. 8.
    Hemmati, M., Amjady, N., Ehsan, M.: System modeling and optimization for islanded micro-grid using multi-cross learning-based chaotic differential evolution algorithm. Int. J. Electr. Power Energy Syst. 56, 349–360 (2014)CrossRefGoogle Scholar
  9. 9.
    Zare, M., Niknam, T., Azizipanah-Abarghooee, R., Ostadi, A.: New stochastic bi-objective optimal cost and chance of operation management approach for smart microgrid. IEEE Trans. Ind. Inform. 12(6), 2031–2040 (2016)CrossRefGoogle Scholar
  10. 10.
    Nayak, M.R., Krishnanand, K.R., Rout, P.K.: Modified differential evolution optimization algorithm for multi-constraint optimal power flow. In: 2011 International Conference on Energy, Automation, and Signal (ICEAS), pp. 1–7. IEEE, 2011 DecemberGoogle Scholar
  11. 11.
    Huang, C.M., Chen, S.J., Huang, Y.C., Yang, S.P.: Optimal active-reactive power dispatch using an enhanced differential evolution algorithm. In: 2011 6th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 1869–1874. IEEE, June 2011Google Scholar
  12. 12.
    Karaboa, D., Okdem, S.: A simple and global optimization algorithm for engineering problems: differential evolution algorithm. Turk. J. Electr. Eng. Comput. Sci. 12(1), 53–60 (2004)Google Scholar
  13. 13.
    Carreiro, A.M., Oliveira, C., Antunes, C.H., Jorge, H.M.: An energy management system aggregator based on an integrated evolutionary and differential evolution approach. In: European Conference on the Applications of Evolutionary Computation, pp. 252–264. Springer International Publishing, April 2015Google Scholar
  14. 14.
    Yu, M., Wang, Y., Li, Y.G.: Energy management of wind turbine-based DC microgrid utilizing modified differential evolution algorithm (2015)Google Scholar
  15. 15.
    Ali, M., Pant, M., Abraham, A.: A modified differential evolution algorithm and its application to engineering problems. In: SoCPaR, pp. 196–201, December 2009Google Scholar
  16. 16.
    Arafa, M., Sallam, E.A., Fahmy, M.M.: An enhanced differential evolution optimization algorithm. In: 2014 Fourth International Conference on Digital Information and Communication Technology and It’s Applications (DICTAP), pp. 216–225. IEEE, May 2014Google Scholar
  17. 17.
    Tiwari, N., Srivastava, L.: Generation scheduling and micro-grid energy management using differential evolution algorithm. In: 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), pp. 1–7. IEEE, March 2016Google Scholar
  18. 18.
    Liu, Y., Rowe, M., Holderbaum, W., Potter, B.: A novel battery network modelling using constraint differential evolution algorithm optimisation. Knowl. Based Syst. 99, 10–18 (2016)CrossRefGoogle Scholar
  19. 19.
    Galvn-Lpez, E., Schoenauer, M., Patsakis, C., Trujillo, L.: Demand-side management: optimising through differential evolution plug-in electric vehicles to partially fulfil load demand. In: Computational Intelligence, pp. 155–174. Springer International Publishing (2015)Google Scholar
  20. 20.
    Zhang, J., Wu, Y., Guo, Y., Wang, B., Wang, H., Liu, H.: A hybrid harmony search algorithm with differential evolution for day-ahead scheduling problem of a microgrid with consideration of power flow constraints. Appl. Energy 183, 791–804 (2016)CrossRefGoogle Scholar
  21. 21.
    Amjady, N., Keynia, F., Zareipour, H.: Short-term load forecast of microgrids by a new bilevel prediction strategy. IEEE Trans. Smart Grid 1(3), 286–294 (2010)CrossRefGoogle Scholar
  22. 22.
    Sayah, S., Zehar, K.: Modified differential evolution algorithm for optimal power flow with non-smooth cost functions. Energy Convers. Manag. 49(11), 3036–3042 (2008)CrossRefGoogle Scholar
  23. 23.
    Basu, A.K., Bhattacharya, A., Chowdhury, S., Chowdhury, S.P.: Planned scheduling for economic power sharing in a CHP-based micro-grid. IEEE Trans. Power Syst. 27(1), 30–38 (2012)CrossRefGoogle Scholar
  24. 24.
    Hui, S., Suganthan, P.N.: Ensemble and arithmetic recombination-based speciation differential evolution for multimodal optimization. IEEE Trans. Cybern. 46(1), 64–74 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan

Personalised recommendations