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Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 130))

Abstract

Differential evolution (DE) is a well known and simple population based probabilistic approach used to solve nonlinear and complex problems. It has reportedly outperformed a few evolutionary algorithms when tested over both benchmark and real world problems. DE, like other probabilistic optimization algorithms, has inherent drawback of premature convergence and stagnation. Therefore, in order to find a trade-off between exploration and exploitation capability of DE algorithm, scaling factor in mutation process is modified. In mutation process, trial vector is calculated by perturbing the target vector. In this paper, a dynamic scale factor is proposed which controls the perturbation rate in mutation process. The proposed strategy is named as Dynamic Scaling Factor based Differential Evolution Algorithm (DSFDE). To prove efficiency of DSFDE, it is tested over 10 benchmark problems.

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References

  1. 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 Transactions on Evolutionary Computation 10(6), 646–657 (2006)

    Article  Google Scholar 

  2. Chakraborty, U.K.: Advances in differential evolution. Springer, Heidelberg (2008)

    Book  MATH  Google Scholar 

  3. Croarkin, C., Tobias, P.: Nist/sematech e-handbook of statistical methods (2010) (retrieved March 1, 2010)

    Google Scholar 

  4. Das, S., Konar, A.: Two-dimensional iir filter design with modern search heuristics: A comparative study. International Journal of Computational Intelligence and Applications 6(3), 329–355 (2006)

    Article  MATH  Google Scholar 

  5. Thakur, M., Deep, K.: A new crossover operator for real coded genetic algorithms. Applied Mathematics and Computation 188(1), 895–911 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  6. Engelbrecht, A.P.: Computational intelligence: an introduction. Wiley (2007)

    Google Scholar 

  7. Gamperle, R., Muller, S.D., Koumoutsakos, A.: A parameter study for differential evolution. Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation 10, 293–298 (2002)

    Google Scholar 

  8. Holland, J.H.: Adaptation in natural and artificial systems (1975)

    Google Scholar 

  9. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE (1995)

    Google Scholar 

  10. Lampinen, J., Zelinka, I.: On stagnation of the differential evolution algorithm. In: Proceedings of MENDEL, Citeseer, pp. 76–83 (2000)

    Google Scholar 

  11. Liu, P.K., Wang, F.S.: Inverse problems of biological systems using multi-objective optimization. Journal of the Chinese Institute of Chemical Engineers 39(5), 399–406 (2008)

    Article  Google Scholar 

  12. Mezura-Montes, E., Velázquez-Reyes, J., Coello Coello, C.A.: A comparative study of differential evolution variants for global optimization. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 485–492. ACM (2006)

    Google Scholar 

  13. Omran, M.G.H., Engelbrecht, A.P., Salman, A.: Differential evolution methods for unsupervised image classification. In: The 2005 IEEE Congress on Evolutionary Computation, vol. 2, pp. 966–973. IEEE (2005)

    Google Scholar 

  14. Onwubolu, G.C., Sharma, A.: Intrusion detection system using hybrid differential evolution and group method of data handling approach

    Google Scholar 

  15. Price, K.V.: Differential evolution: a fast and simple numerical optimizer. In: 1996 Biennial Conference of the North American Fuzzy Information Processing Society, NAFIPS, pp. 524–527. IEEE (1996)

    Google Scholar 

  16. Price, K.V., Storn, R.M., Lampinen, J.A.: Differential evolution: a practical approach to global optimization. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  17. Rogalsky, T., Kocabiyik, S., Derksen, R.W.: Differential evolution in aerodynamic optimization. Canadian Aeronautics and Space Journal 46(4), 183–190 (2000)

    Google Scholar 

  18. Storn, R., Price, K.: Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. International Computer Science Institute-Publications-TR (1995)

    Google Scholar 

  19. 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, CEC 2004, vol. 2, pp. 1980–1987. IEEE (2004)

    Google Scholar 

  20. Williamson, D.F., Parker, R.A., Kendrick, J.S.: The box plot: a simple visual method to interpret data. Annals of Internal Medicine 110(11), 916 (1989)

    Google Scholar 

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Correspondence to Harish Sharma .

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Sharma, H., Bansal, J.C., Arya, K.V. (2012). Dynamic Scaling Factor Based Differential Evolution Algorithm. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 130. Springer, India. https://doi.org/10.1007/978-81-322-0487-9_8

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  • DOI: https://doi.org/10.1007/978-81-322-0487-9_8

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