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Modified Single Array Selection Operation for DE Algorithm

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Proceedings of Fifth International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 437))

Abstract

In this study, a modified selection operation is proposed for differential evaluation (DE) algorithm. The proposed selection strategy called information utilization (IU) strategy and the proposed DE variant called IUDE reuse redundant trial vectors embedded with single array selection strategy. The proposed selection strategy is implemented on DERL and MRLDE, the enhanced DE variants and the corresponding algorithms are termed IU-DERL and IU-MRLDE. Six traditional functions are taken for experiments. Results confirm that the proposed selection strategy is helpful in amplifying the convergence speed.

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References

  1. Storn, R., Price, K.: Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous. Spaces. Berkeley, CA, Tech. Rep. TR-95-012 (1995)

    Google Scholar 

  2. Vesterstrom, J., Thomsen, R.: A comparative study of differential evolution, particle swarm optimization and evolutionary algorithms on numerical benchmark problems. In: Congress on Evolutionary Computation, pp. 980–987 (2004)

    Google Scholar 

  3. Plagianakos, V., Tasoulis, D., Vrahatis, M.: A review of major application areas of differential evolution. Adv. Diff. Evol. 143, 197–238 (2008). Springer, Berlin

    Article  Google Scholar 

  4. Kumar, P., Kumar, S., Pant, M.: Gray level image enhancement by improved differential evolution algorithm. Proc. BICTA 2(2012), 443–453 (2012)

    Google Scholar 

  5. Kumar, S., Kumar, P., Sharma, T.K., Pant, M.: Bi-level thresholding using PSO, artificial bee colony and MRLDE embedded with Otsu method. Memetic Comput. 5(4), 323–334 (2013)

    Article  Google Scholar 

  6. Kumar, P., Pant, M.: Noisy source recognition in multi noise plants by differential evolution. Proc. SIS 2013, 271–275 (2013)

    Google Scholar 

  7. Ali, M., Ahn, C.W., Pant, M.: Multi-level image thresholding by synergetic differential evolution. Appl. Soft Comput. 17, 1–11 (2014)

    Article  Google Scholar 

  8. Kumar, P., Pant, M., Singh, V.P.: Modified random localization based DE for static economic power dispatch with generator constraints. Int. J. Bio-Inspired Comput. 6(4), 250–261 (2014)

    Article  Google Scholar 

  9. Fan, H., Lampinen, J.: A trigonometric mutation operation to differentia evolution. J. Global Opt. 27, 105–129 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  10. Liu, J., Lampinen, J.: A fuzzy adaptive differential evolution algorithm. Soft Comput. Fus. Found Methodol. Appl. 9(6), 448–462 (2005)

    MATH  Google Scholar 

  11. Kaelo, P., Ali, M.M.: A numerical study of some modified differential evolution algorithms. Eur. J. Oper. Res. 169, 1176–1184 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  12. Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V.: Self adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10(6), 646–657 (2006)

    Article  Google Scholar 

  13. Noman, N., Iba, H.: Accelerating differential evolution using an adaptive local Search. IEEE Trans. Evol. Comput. 12(1), 107–125 (2008)

    Article  Google Scholar 

  14. Rahnamayan, S., Tizhoosh, H., Salama, M.: Opposition based differential evolution. IEEE Trans. Evol. Comput. 12(1), 64–79 (2008)

    Article  Google Scholar 

  15. Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13(2), 398–417 (2009)

    Article  Google Scholar 

  16. Zhang, J., Sanderson, A.: JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13(5), 945–958 (2009)

    Article  Google Scholar 

  17. Ali, M., Pant, M.: Improving the performance of differential evolution algorithm using cauchy mutation. Soft Comput. (2010). doi:10.1007/s00500-010-0655-2

    Google Scholar 

  18. Kumar, P., Pant, M.: Enhanced mutation strategy for differential evolution. In: Proceeding of IEEE Congress on Evolutionary Computation (CEC-12), pp. 1–6 (2012)

    Google Scholar 

  19. Sarker, R.A., Elsayed, S.M., Ray, T.: Differential evolution with dynamic parameters selection for optimization problems. IEEE Trans. Evol. Comput. 18(5), 689–707 (2014)

    Article  Google Scholar 

  20. Pooja, C.P., Kumar, P.: A cultivated differential evolution variant for molecular potential energy problem. Procedia Comput. Sci. 57, 1265–1272 (2015)

    Article  Google Scholar 

  21. Neri, F., Tirronen, V.: Recent advances in differential evolution: a survey and experimental analysis. Artif Intell. Rev. 33(1–2), 61–106 (2010)

    Article  Google Scholar 

  22. Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–13 (2011)

    Article  Google Scholar 

  23. Gonuguntla, V., Mallipeddi, R., Veluvolu, K.C.: Differential evolution with population and strategy parameter adaptation. In: Mathematical Problems in Engineering (2015), doi:http://dx.doi.org/10.1155/2015/287607 (2015)

  24. Babu, B.V., Angira, R.: Modified differential evolution (MDE) for optimization of non-linear chemical processes. Comput. Chem. Eng. 30, 989–1002 (2006)

    Google Scholar 

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Correspondence to Pravesh Kumar .

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Pravesh Kumar, Pant, M. (2016). Modified Single Array Selection Operation for DE Algorithm. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 437. Springer, Singapore. https://doi.org/10.1007/978-981-10-0451-3_71

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  • DOI: https://doi.org/10.1007/978-981-10-0451-3_71

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0450-6

  • Online ISBN: 978-981-10-0451-3

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