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Modified differential evolution with self-adaptive parameters method

Author

Listed:
  • Xiangtao Li

    (Northeast Normal University)

  • Minghao Yin

    (Northeast Normal University)

Abstract

The differential evolution algorithm (DE) is a simple and effective global optimization algorithm. It has been successfully applied to solve a wide range of real-world optimization problem. In this paper, the proposed algorithm uses two mutation rules based on the rand and best individuals among the entire population. In order to balance the exploitation and exploration of the algorithm, two new rules are combined through a probability rule. Then, self-adaptive parameter setting is introduced as uniformly random numbers to enhance the diversity of the population based on the relative success number of the proposed two new parameters in a previous period. In other aspects, our algorithm has a very simple structure and thus it is easy to implement. To verify the performance of MDE, 16 benchmark functions chosen from literature are employed. The results show that the proposed MDE algorithm clearly outperforms the standard differential evolution algorithm with six different parameter settings. Compared with some evolution algorithms (ODE, OXDE, SaDE, JADE, jDE, CoDE, CLPSO, CMA-ES, GL-25, AFEP, MSAEP and ENAEP) from literature, experimental results indicate that the proposed algorithm performs better than, or at least comparable to state-of-the-art approaches from literature when considering the quality of the solution obtained.

Suggested Citation

  • Xiangtao Li & Minghao Yin, 2016. "Modified differential evolution with self-adaptive parameters method," Journal of Combinatorial Optimization, Springer, vol. 31(2), pages 546-576, February.
    • Handle: RePEc:spr:jcomop:v:31:y:2016:i:2:d:10.1007_s10878-014-9773-6
    DOI: 10.1007/s10878-014-9773-6
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    References listed on IDEAS

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    1. Garcia-Martinez, C. & Lozano, M. & Herrera, F. & Molina, D. & Sanchez, A.M., 2008. "Global and local real-coded genetic algorithms based on parent-centric crossover operators," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1088-1113, March.
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    Cited by:

    1. Shijing Ma & Xiangtao Li & Yunhe Wang, 2016. "Classification of Gene Expression Data Using Multiobjective Differential Evolution," Energies, MDPI, vol. 9(12), pages 1-22, December.

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