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International Conference on Swarm Intelligence

ICSI 2018: Advances in Swarm Intelligence pp 225–234Cite as

An Improved Blind Optimization Algorithm for Hardware/Software Partitioning and Scheduling

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10942))

Abstract

Hardware/software partitioning is an important part in the development of complex embedded system. Blind optimization algorithms are suitable to solve the problem when it is combined with task scheduling. To get hardware/software partitioning algorithms with higher performance, this paper improves Shuffled Frog Leaping Algorithm-Earliest Time First (SFLA-ETF) which is a blind optimization algorithm. Under the supervision of the aggregation factor, the improved algorithm named Supervised SFLA-ETF (SSFLA-ETF) used two steps to better balance exploration and exploitation. Experimental results show that compared with SFLA-ETF and other swarm intelligence algorithms, SSFLA-ETF has stronger optimization ability.

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Author information

Authors and Affiliations

  1. School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China

    Xin Zhao, Tao Zhang, Xinqi An & Long Fan

  2. Texas Instruments DSP Joint Lab, Tianjin University, Tianjin, 300072, China

    Xin Zhao, Tao Zhang, Xinqi An & Long Fan

Authors
  1. Xin Zhao
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  2. Tao Zhang
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  3. Xinqi An
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  4. Long Fan
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Corresponding author

Correspondence to Tao Zhang .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Tongji University, Shanghai, China

    Qirong Tang

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© 2018 Springer International Publishing AG, part of Springer Nature

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Zhao, X., Zhang, T., An, X., Fan, L. (2018). An Improved Blind Optimization Algorithm for Hardware/Software Partitioning and Scheduling. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10942. Springer, Cham. https://doi.org/10.1007/978-3-319-93818-9_21

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  • DOI: https://doi.org/10.1007/978-3-319-93818-9_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93817-2

  • Online ISBN: 978-3-319-93818-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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