Skip to main content
SpringerLink
Log in

A Novel Differential Evolution Algorithm Based on JADE for Constrained Optimization

  • Conference paper
  • First Online:
Computational Intelligence and Intelligent Systems (ISICA 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 575))

Abstract

To overcome the problem of slow convergence and easy to be plunged to premature when the traditional differential evolution algorithm for solving constrained optimization problems, a novel differential evolution algorithm (CO-JADE) based on adaptive differential evolution (JADE) for constrained optimization was proposed. The algorithm used skew tent chaotic mapping to initialize the population, generated the crossover probability of each individual according to the normal distribution and the Cauchy distribution and the mutation factor according to the normal distribution. CO-JADE used improved adaptive tradeoff model to evaluate the individuals of population. The improved adaptive tradeoff model used different treatment scheme for different stages of population, which aimed to effectively weigh the relationship between the value of the objective function and the degree of constraint violation. Simulation experiments were conducted on the night standard test functions. CO-JADE was much better than COEA/ODE and HCOEA in terms of the accuracy and standard variance of final solution. The experimental results demonstrate that the CO-JADE has better accuracy and stability.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Lee, G.: A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput. Methods Appl. Mech. Eng. 194, 3902–3933 (2005)

    Article  MATH  Google Scholar 

  2. Holland, J.H.: Adaptation in Natural and Artificial Systems. Mich: University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  3. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company, Massachusetts (1989)

    MATH  Google Scholar 

  4. 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 

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

    Google Scholar 

  6. Wang, Y., Cai, Z., Zhou, Y., et al.: Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint-handling technique. Struct. Multi. Optim. 37(4), 395–413 (2009)

    Article  Google Scholar 

  7. Liu, H., Cai, Z., Wang, Y.: Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl. Soft Comput. 10(2), 629–640 (2010)

    Article  Google Scholar 

  8. Zi-Xing, C., Zhong-Yang, J., Yong, W., et al.: A novel constarained optimization evolutionary algorithm based on orthogonal experimental design. J. Comput. Sci. 33(5), 855–864 (2010)

    Google Scholar 

  9. Ning, D., Yuping, W.: Multi-objective evolutionary algorithm based on preference for constrained optimization problems. J. Xidian Univ. 41(1), 98–104 (2014)

    Google Scholar 

  10. 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 

  11. Zhenyu, G., Bo, C., Min, Y., et al.: Parallel chaos differential evolution algorithm. J. Xi’an Jiaotong Univ. 41(3), 299–302 (2007)

    MATH  Google Scholar 

  12. Jia, G., Wang, Y., Cai, Z., Jin, Y.: An improved (μ + λ)-constrained differential evolution for constrained optimization. Inf. Sci. 222, 302–322 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  13. Liang, J.J., Runarsson, T.P., Mezura-Montes, E., et al.: Problem Definitions and Evaluation Criteria for the CEC 2006 Special Session on Constrained Real-Parameter Optimization. Nanyang Technological University, Singapore (2006)

    Google Scholar 

  14. Wang, Y., Cai, Z., Guo, G., et al.: Multiobjective optimization and hybrid evolutionary algorithm to solve constrained optimization problems. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 37(3), 560–575 (2007)

    Article  Google Scholar 

  15. Wang, Y., Cai, Z., Zhou, Y., et al.: An adaptive tradeoff model for constrained evolutionary optimization. IEEE Trans. Evol. Comput. 12(1), 80–92 (2008)

    Article  Google Scholar 

  16. Mezura-Montes, E., Cetina-Domínguez, O.: Empirical analysis of a modified artificial bee colony for constrained numerical optimization. Appl. Math. Comput. 218(22), 10943–10973 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  17. Farmani, R., Wright, J.A.: Self-adaptive fitness formulation for constrained optimization. IEEE Trans. Evol. Comput. 7(5), 445–455 (2003)

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China with the Grant No. 61573157, the Fund of Natural Science Foundation of Guangdong Province of China with the Grant No. 2014A030313454, the Natural Science Foundation of Guangdong Province of China with the Grant No. 2015A030313408.

Author information

Authors and Affiliations

  1. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China

    Kangshun Li, Lei Zuo, Wei Li & Lei Yang

  2. School of Information Engineering, JiangXi University of Science and Technology, Ganzhou, 341000, China

    Kangshun Li & Wei Li

Authors
  1. Kangshun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lei Zuo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lei Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lei Zuo .

Editor information

Editors and Affiliations

  1. College of Mathematics and Informatics, The South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. School of Computer Science, Guangzhou University, Guangzhou, China

    Jin Li

  3. School of Computer Science and Engineeri, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

  4. Dept. of Informatics, University of Salerno, Fisciano, Italy

    Aniello Castiglione

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Science+Business Media Singapore

About this paper

Cite this paper

Li, K., Zuo, L., Li, W., Yang, L. (2016). A Novel Differential Evolution Algorithm Based on JADE for Constrained Optimization. In: Li, K., Li, J., Liu, Y., Castiglione, A. (eds) Computational Intelligence and Intelligent Systems. ISICA 2015. Communications in Computer and Information Science, vol 575. Springer, Singapore. https://doi.org/10.1007/978-981-10-0356-1_9

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-0356-1_9

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0355-4

  • Online ISBN: 978-981-10-0356-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation