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A Mean Shift Assisted Differential Evolution Algorithm

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Bio-inspired Computing – Theories and Applications (BIC-TA 2016)

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

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Abstract

It is well known that Differential Evolution (DE) algorithm has been widely applied to solve global optimization problems during the last decades. DE is usually criticized for the slow convergence. To improve the algorithm performance, we propose an algorithm called MSDE that utilizes a local search operator based on mean shift. In MSDE, one offspring solution is generated by the mean shift based search operator, and the others are created by the DE search operator. A test suite of 12 benchmark functions with different characteristics are chosen to evaluate our approach. The experimental results suggest that MSDE can successfully improve the performance of DE and have a faster convergence rate on the given test suite.

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Acknowledgment

This work is supported by China National Instrumentation Program under Grant No. 2012YQ180132, the National Natural Science Foundation of China under Grant No. 61273313 and No. 61673180, and the Science and Technology Commission of Shanghai Municipality under Grant No. 14DZ2260800.

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Authors and Affiliations

  1. Shanghai Key Laboratory of Multidimensional Information Processing, Department of Computer Science and Technology, East China Normal University, Shanghai, 200241, China

    Hui Fang, Aimin Zhou & Guixu Zhang

Authors
  1. Hui Fang
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  2. Aimin Zhou
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  3. Guixu Zhang
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Corresponding author

Correspondence to Hui Fang .

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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Fang, H., Zhou, A., Zhang, G. (2016). A Mean Shift Assisted Differential Evolution Algorithm. 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_21

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  • DOI: https://doi.org/10.1007/978-981-10-3614-9_21

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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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