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Evolutionary Process: Parallelism Analysis of Differential Evolution Algorithm Based on Graph Theory

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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

Computation intelligence is becoming an essential technology during the development of human society. There are many family members in computation intelligence. Many researchers have already studied the mathematical inherent rules of these biology-inspired algorithms to found methods to improve the capacity of the algorithms. In the family of computation intelligence, differential evolution (DE) algorithm shows performance optimization ability. In order to explore the reason why DE could have stable and robust quality. We analyzed the parallelism of the evolutionary process in the iterative process of DE algorithm based on graph theory. By the knowledge of graph theory, it will directly exhibit the essential reason of differential evolution in the algorithm. The research will reveal that the superior DE algorithm have more extent parallelism ability than the elementary algorithm.

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Acknowledgments

This work is supported in part by NSFC-Guangdong Joint Found (U1501254), National Natural Science Foundation of China (61370102, 61472089, 61572143), Natural Science Foundation of Guangdong(2014A030306004, 2014A030308008), Science and Technology Planning Project of Guangdong (2013B 051000076, 2015B010108006, 2015B010131015), Guangdong Natural Science Funds for Distinguished Young Scholar (2014A030306050), the Fundamental Research Funds for the Central Universities, SCUT (2015PT022) and Guangdong High-level personnel of special support program (2014TQ01X664).

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Authors and Affiliations

  1. School of Applied Mathematics, Guangdong University of Technology, Guangzhou, 510520, China

    Xiaoqi Peng

  2. School of Mathematics and Big Data, Foshan University, Foshan, 528000, China

    Zhifeng Hao

  3. School of Software Engineering, South China University of Technology, Guangzhou, 510006, China

    Han Huang, Hongyue Wu & Fangqing Liu

Authors
  1. Xiaoqi Peng
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  2. Zhifeng Hao
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  3. Han Huang
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  4. Hongyue Wu
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  5. Fangqing Liu
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Corresponding author

Correspondence to Han Huang .

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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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Peng, X., Hao, Z., Huang, H., Wu, H., Liu, F. (2016). Evolutionary Process: Parallelism Analysis of Differential Evolution Algorithm Based on Graph Theory. 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_20

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  • DOI: https://doi.org/10.1007/978-981-10-3614-9_20

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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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