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RDE - Reconstructed Mutation Strategy for Differential Evolution Algorithm

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Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016) (SoCPaR 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 614))

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Abstract

Several researchers’ innovative work during past years has led to development of numerous optimization techniques. Complex task that were once difficult to be compute using traditional methods now can use the optimization techniques for computation. Differential Evolution (DE) is a powerful, population based, stochastic optimization algorithm. The mutation strategy of DE algorithm is an important operator as it aids in generating a new solution vector. In this paper, we are introducing a variant of DE mutation strategy named RDE (Reconstructed Differential Evolution). This strategy use three different control parameters. The results computed here are then compared with the results of an existing mutation strategy where in the comparison show a better performance for the new revised strategy.

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References

  1. Ali, M., Pant, M., Abraham, A.: Simplex differential evolution. Acta Polytech. Hung. 6(5), 95–115 (2009)

    Google Scholar 

  2. 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 

  3. Das, S., Abraham, A., Konar, A.: Particle swarm optimization and differential evolution algorithms: technical analysis, applications and hybridization perspectives. In: Advances of Computational Intelligence in Industrial Systems, pp. 1–38. Springer, Heidelberg (2008)

    Google Scholar 

  4. Das, S., Konar, A., Chakraborty, U.K.: Two improved differential evolution schemes for faster global search. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, pp. 991–998. ACM (2005)

    Google Scholar 

  5. Gong, W., Cai, Z.: Differential evolution with ranking-based mutation operators. IEEE Trans. Cybern. 43(6), 2066–2081 (2013)

    Article  Google Scholar 

  6. Gong, W., Cai, Z., Ling, C.X., Li, H.: Enhanced differential evolution with adaptive strategies for numerical optimization. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 41(2), 397–413 (2011)

    Article  Google Scholar 

  7. Grosan, C., Abraham, A.: Hybrid evolutionary algorithms: methodologies, architectures, and reviews. In: Hybrid Evolutionary Algorithms, pp. 1–17. Springer, Heidelberg (2007)

    Google Scholar 

  8. Islam, S.M., Das, S., Ghosh, S., Roy, S., Suganthan, P.N.: An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 42(2), 482–500 (2012)

    Article  Google Scholar 

  9. Ramadas, M., Pant, M., Abraham, A., Kumar, S.: ssFPA/DE: An efficient hybrid differential evolution–flower pollination algorithm based approach. Int. J. Syst. Assur. Eng. Manag. 1–14 (2016)

    Google Scholar 

  10. Ramadas, M., Abraham, A., Kumar, S.: ReDE- A revised mutation strategy for differential evolution algortihm. Int. J. Intell. Eng. Syst. 9(4), 51–58 (2016)

    Google Scholar 

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

    Article  Google Scholar 

  12. Qiu, X., Xu, J.-X., Tan, K.C., Abbass, H.A.: Adaptive cross-generation differential evolution operators for multiobjective optimization. IEEE Trans. Evol. Comput. 20(2), 232–244 (2016)

    Article  Google Scholar 

  13. Storn, R., Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  14. Tang, L., Dong, Y., Liu, J.: Differential evolution with an individual-dependent mechanism. IEEE Trans. Evol. Comput. 19(4), 560–574 (2015)

    Article  Google Scholar 

  15. Xin, B., Chen, J., Peng, Z., Pan, F.: An adaptive hybrid optimizer based on particle swarm and differential evolution for global optimization. Sci. China Inf. Sci. 53(5), 980–989 (2010)

    Article  MathSciNet  Google Scholar 

  16. Yu, W.J., Shen, M., Chen, W.N., Zhan, Z.H., Gong, Y.J., Lin, Y., Liu, O., Zhang, J.: Differential evolution with two-level parameter adaptation. IEEE Trans. Cybern. 44(7), 1080–1099 (2014)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. AIIT, Amity University, Noida, India

    Meera Ramadas

  2. MIR Labs, Auburn, USA

    Ajith Abraham

  3. ASET, Amity University, Noida, India

    Sushil Kumar

Authors
  1. Meera Ramadas
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  2. Ajith Abraham
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  3. Sushil Kumar
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Corresponding author

Correspondence to Meera Ramadas .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research, Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA

    Ajith Abraham

  2. VIT University, Vellore, Tamil Nadu, India

    Aswani Kumar Cherukuri

  3. School of Engineering, Polytechnic of Porto (ISEP/IPP), Porto, Portugal

    Ana Maria Madureira

  4. Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia

    Azah Kamilah Muda

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Ramadas, M., Abraham, A., Kumar, S. (2018). RDE - Reconstructed Mutation Strategy for Differential Evolution Algorithm. In: Abraham, A., Cherukuri, A., Madureira, A., Muda, A. (eds) Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016). SoCPaR 2016. Advances in Intelligent Systems and Computing, vol 614. Springer, Cham. https://doi.org/10.1007/978-3-319-60618-7_8

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  • DOI: https://doi.org/10.1007/978-3-319-60618-7_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60617-0

  • Online ISBN: 978-3-319-60618-7

  • eBook Packages: EngineeringEngineering (R0)

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