Skip to main content
SpringerLink
Log in

Differential Evolution Improved with Adaptive Control Parameters and Double Mutation Strategies

  • Conference paper
  • First Online:
Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems (AsiaSim 2016, SCS AutumnSim 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 643))

Included in the following conference series:

Abstract

Recently, differential evolution (DE) algorithm has attracted more and more attention as an excellent and effective approach for solving numerical optimization problems. However, it is difficult to set suitable mutation strategies and control parameters. In order to solve this problem, in this paper a dynamic adaptive double-model differential evolution (DADDE) algorithm for global numerical optimization is proposed, and dynamic random search (DRS) strategy is introduced to enhance global search capability of the algorithm. The simulation results of ten benchmark show that the proposed DADDE algorithm is better than several other intelligent optimization algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. Storn, R., Price, K.V.: Differential evolution: A simple and efficient adaptive scheme for global optimization over continuous spaces, ICSI, USA, Technical Report TR-95–012 (1995)

    Google Scholar 

  2. Storn, R., Price, K.V.: Minimizing the real functions of the ICEC 1996 contest by differential evolution. In: Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, pp. 842–844 (1996)

    Google Scholar 

  3. Storn, R.: On the usage of differential evolution for function optimization. In: Proceedings of the North American Fuzzy Information Processing Society Conference, pp. 519–523 (1996)

    Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  5. Secmen, M., Tasgetiren, M.F.: Ensemble of differential evolution algorithms for electromagnetic target recognition problem. IET Radar Sonar Navig. 7(7), 780–788 (2013)

    Article  Google Scholar 

  6. Sharma, S., Rangaiah, G.P.: An improved multi-objective differential evolution with a termination criterion for optimizing chemical processes. Comput. Chem. Eng. 56, 155–173 (2013)

    Article  Google Scholar 

  7. Zhu, J.X., Wen, X.B., Xu, H.X., Sun, L.Q.: Image sparse decomposition and reconstruction based on differential evolution algorithm. Adv. Inf. Sci. Serv. Sci. 3(10), 131–137 (2011)

    Google Scholar 

  8. Daniela, Z.: Influence of crossover on the behavior of differential evolution algorithms. Appl. Soft Comput. 9(3), 1126–1138 (2009)

    Article  Google Scholar 

  9. Brest, J., Mernik, M.: Population size reduction for the differential evolution algorithm. Appl. Intell. 29(3), 228–247 (2008)

    Article  Google Scholar 

  10. Qin, A.K., Suganthan, P.N.: Self-adaptive differential evolution algorithm for numerical optimization. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC 2005), pp. 1785–1791. IEEE Press, Edinburgh, Scotland (2005)

    Google Scholar 

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

    Article  Google Scholar 

  12. Hamzacebi, C., Kutay, F.: Continuous functions minimization by dynamic random search technique. Appl. Math. Model. 31(10), 2189–2198 (2007)

    Article  MATH  Google Scholar 

  13. Hamzacebi, C., Kutay, F.: A heuristic approach for finding the global minimum: adaptive random search technique. Appl. Math. Comput. 173(2), 1323–1333 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  14. Hu, Z.Q.: The optimization of differential evolution algorithm and its application research, pp. 28–30 (2013). (in Chinese)

    Google Scholar 

  15. Yao, X., Liu, Y., Lin, G.M.: Evolutionary programming made faster. IEEE Trans. Evol. Comput. 3(2), 82–102 (1999)

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 61573144, 61174040), Shanghai Commission of Science and Technology (Grant no. 12JC1403400), and the Fundamental Research Funds for the Central Universities.

Author information

Authors and Affiliations

  1. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China

    Jun Liu, Xiaoming Yin & Xingsheng Gu

Authors
  1. Jun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoming Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xingsheng Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xingsheng Gu .

Editor information

Editors and Affiliations

  1. Beihang University, Beijing, China

    Lin Zhang

  2. Beihang University, Beijing, China

    Xiao Song

  3. Beihang University, Beijing, China

    Yunjie Wu

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Science+Business Media Singapore

About this paper

Cite this paper

Liu, J., Yin, X., Gu, X. (2016). Differential Evolution Improved with Adaptive Control Parameters and Double Mutation Strategies. In: Zhang, L., Song, X., Wu, Y. (eds) Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems. AsiaSim SCS AutumnSim 2016 2016. Communications in Computer and Information Science, vol 643. Springer, Singapore. https://doi.org/10.1007/978-981-10-2663-8_20

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-2663-8_20

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2662-1

  • Online ISBN: 978-981-10-2663-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation