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Experimental Study of Distributed Differential Evolution Based on Different Platforms

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Book cover Bio-inspired Computing: Theories and Applications (BIC-TA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 791))

Abstract

With the increasing complexity of real-world optimization problems, many challenges appear to evolutionary algorithms (EAs). When solving these time-consuming or high-complexity problems, although EAs can guarantee the high quality of solutions, the intolerable time costs will influence their availabilities drastically. Thus, many attempts have been made to overcome that problem. With the rapid development of the distributed computing paradigm and platforms, such as the Message Passing Interface (MPI) and Open Multi-Processing (OpenMP), distributed computing has become readily available and affordable for realizing more powerful EAs. In order to find out whether these platforms have any particular difficulties or preference, whether one of them would be more suitable for EAs, we analyze the performance of different distributed EAs (DEAs) based on different distributed computing platforms, using differential evolution (DE) as an example. Finally, we find out that both MPI and OpenMP have their own superiorities and they can improve the speedup obviously. However, MPI is more suitable for computationally expensive problems and can achieve higher speedup than OpenMP.

L. Shi—Student Member, IEEE; Z-H. Zhan, Member, IEEE; Z-J. Wang, Student Member, IEEE; J. Zhang, Fellow, IEEE.

This work was partially supported by the National Natural Science Foundations of China (NSFC) with Nos. 61772207, 61402545, and 61332002, the Natural Science Foundations of Guangdong Province for Distinguished Young Scholars with No. 2014A030306038, the Project for Pearl River New Star in Science and Technology with No. 201506010047, the GDUPS (2016), and the Fundamental Research Funds for the Central Universities.

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Acknowledgments

This work was partially supported by the National Natural Science Foundations of China (NSFC) with Nos. 61772207, 61402545, and 61332002, the Natural Science Foundations of Guangdong Province for Distinguished Young Scholars with No. 2014A030306038, the Project for Pearl River New Star in Science and Technology with No. 201506010047, the GDUPS (2016), and the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China

    Lin Shi, Zhi-Hui Zhan, Zi-Jia Wang & Jun Zhang

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  1. Lin Shi
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  2. Zhi-Hui Zhan
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  3. Zi-Jia Wang
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  4. Jun Zhang
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Correspondence to Zhi-Hui Zhan .

Editor information

Editors and Affiliations

  1. School of Automation, Huazhong University of Science and Technology, Wuhan, China

    Cheng He

  2. Automation College, Harbin Engineering University, Harbin, China

    Hongwei Mo

  3. School of Automation, Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  4. Automation College, Harbin Engineering University, Harbin, China

    Yuxin Zhao

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Shi, L., Zhan, ZH., Wang, ZJ., Zhang, J. (2017). Experimental Study of Distributed Differential Evolution Based on Different Platforms. In: He, C., Mo, H., Pan, L., Zhao, Y. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2017. Communications in Computer and Information Science, vol 791. Springer, Singapore. https://doi.org/10.1007/978-981-10-7179-9_37

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  • DOI: https://doi.org/10.1007/978-981-10-7179-9_37

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  • Print ISBN: 978-981-10-7178-2

  • Online ISBN: 978-981-10-7179-9

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