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A Matrix-Based Implementation of DE Algorithm: The Compensation and Deficiency

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Advances in Artificial Intelligence: From Theory to Practice (IEA/AIE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10350))

Abstract

Differential Evolution has become a very popular continuous optimization algorithm since its inception as its simplicity, easy coding and good performance over kinds of optimization problems. Difference operator in donor vector calculation is the key feature of DE algorithm. Usually, base vector and difference vectors selection in calculating a donor usually cost extra lines of condition judgement. Moreover, these vectors are not equally selected from the individual population. These lead to more perturbation in optimization performance. To tackling this disadvantage of DE implementation, a matrix-based implementation of DE algorithm is advanced herein this paper. Three commonly used DE implementation approaches in literature are also presented and contrasted. CEC2013 test suites for real-parameter optimization are used as the test-beds for these comparison. Experiment results show that the proposed matrix-based implementation of DE algorithm performs better on optimization performance than the common implementation schemes of DE algorithm with similar time complexity.

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Notes

  1. 1.

    http://www3.ntu.edu.sg/home/epnsugan/ EA benchmark/CEC Competitions.

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Acknowledgement

This work is funded by Shenzhen Innovation and Entrepreneurship Project with the project number: GRCK20160826105935160.

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Authors and Affiliations

  1. Fujian Provincial Key Lab of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China

    Jeng-Shyang Pan

  2. Department of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China

    Zhenyu Meng

  3. Department of Computer Science and Technology, Xiamen University of Technology, Xiamen, China

    Huarong Xu

  4. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Xiaoqing Li

Authors
  1. Jeng-Shyang Pan
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  2. Zhenyu Meng
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  3. Huarong Xu
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  4. Xiaoqing Li
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Corresponding author

Correspondence to Zhenyu Meng .

Editor information

Editors and Affiliations

  1. Artois University, Lens, France

    Salem Benferhat

  2. Artois University, Lens, France

    Karim Tabia

  3. Texas State University, San Marcos, Texas, USA

    Moonis Ali

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Pan, JS., Meng, Z., Xu, H., Li, X. (2017). A Matrix-Based Implementation of DE Algorithm: The Compensation and Deficiency. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10350. Springer, Cham. https://doi.org/10.1007/978-3-319-60042-0_8

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  • DOI: https://doi.org/10.1007/978-3-319-60042-0_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60041-3

  • Online ISBN: 978-3-319-60042-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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