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International Conference on Bio-Inspired Computing: Theories and Applications

BIC-TA 2016: Bio-inspired Computing – Theories and Applications pp 73–79Cite as

Ensemble of Different Parameter Adaptation Techniques in Differential Evolution

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 682))

Abstract

Differential evolution has been proved to be one of the most powerful evolutionary algorithms for the numerical optimization. However, the performance of differential evolution is significantly influenced by its parameter settings. To remedy this limitation, different parameter adaptation techniques are proposed in the literature. Generally, different parameter adaptation techniques have different rationales and may be suitable to different problems. Based on this consideration, in this paper, we attempt to develop the ensemble of different parameter adaptation techniques to enhance the performance of differential evolution. In our proposed method, different parameter adaptation techniques are combined together to adjust the parameters of different solutions in the population. As an illustration, two parameter adaptation techniques proposed in the literature are used in our proposed method. To verify the performance of our proposal, the functions proposed in CEC 2005 are chosen as the test suite. Experimental results indicate that, on the whole, our proposed method is able to provide better results than the single parameter adaptation based differential evolution variants with respect to the non-parametric statistical tests.

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Notes

  1. 1.

    Due to the tight space limitation, jDE and SHADE are not described in this paper. More details can be found in the corresponding references in [7, 10], respectively.

  2. 2.

    Note that, in this work, the non-parametric statistical tests are calculated by the KEEL software [15], which is available online at http://www.keel.es/.

References

  1. Bäck, T., Hammel, U., Schwefel, H.P.: Evolutionary computation: comments on the history and current state. IEEE Trans. Evol. Comput. 1(1), 3–17 (1997)

    Article  Google Scholar 

  2. 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  MATH  MathSciNet  Google Scholar 

  3. Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)

    Article  Google Scholar 

  4. Gämperle, R., Müler, S., Koumoutsakos, P.: A parameter study for differential evolution. In: Proceedings of the WSEAS International Conference Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, pp. 293–298 (2002)

    Google Scholar 

  5. Eiben, Á.E., Hinterding, R., Michalewicz, Z.: Parameter control in evolutionary algorithms. IEEE Trans. Evol. Comput. 3(2), 124–141 (1999)

    Article  Google Scholar 

  6. Karafotias, G., Hoogendoorn, M., Eiben, A.E.: Parameter control in evolutionary algorithms: trends and challenges. IEEE Trans. Evol. Comput. 19(2), 167–187 (2015)

    Article  Google Scholar 

  7. Brest, J., Greiner, S., Bošković, B., Mernik, M., Žumer, 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 

  8. 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 

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

    Article  Google Scholar 

  10. Tanabe, R., Fukunaga, A.: Success-history based parameter adaptation for differential evolution. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 71–78 (2013)

    Google Scholar 

  11. Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.P., Auger, A., Tiwari, S.: Problem definitions and evaluation criteria for the CEC_2005 special session on real-parameter optimization (2005)

    Google Scholar 

  12. Brest, J., Zumer, V., Maucec, M.: Self-adaptive differential evolution algorithm in constrained real-parameter optimization. In: IEEE Congress on Evolutionary Computation, pp. 215–222 (2006)

    Google Scholar 

  13. Wang, Y., Cai, Z., Zhang, Q.: Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans. Evol. Comput. 15(1), 55–66 (2011)

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. Alcalá-Fdez, J., Sánchez, L., García, S.: KEEL: a software tool to assess evolutionary algorithms to data mining problems. Soft Comput. 13, 307–318 (2015)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computer Science, China University of Geosciences, Wuhan, 430074, People’s Republic of China

    Liang Wang & Wenyin Gong

Authors
  1. Liang Wang
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  2. Wenyin Gong
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Corresponding author

Correspondence to Wenyin Gong .

Editor information

Editors and Affiliations

  1. Xidian University , Xi’an, China

    Maoguo Gong

  2. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  3. China University of Petroleum, Qingdao, China

    Tao Song

  4. Southwest Jiaotong University , Chengdu, China

    Gexiang Zhang

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© 2016 Springer Nature Singapore Pte Ltd.

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Wang, L., Gong, W. (2016). Ensemble of Different Parameter Adaptation Techniques in Differential Evolution. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-10-3614-9_9

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  • DOI: https://doi.org/10.1007/978-981-10-3614-9_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3613-2

  • Online ISBN: 978-981-10-3614-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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