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Simulated Annealing Algorithm Based on Gauss Distribution

Received: 17 April 2016    Accepted:     Published: 18 April 2016
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Abstract

This paper first describes in a brief manner the principle, the building blocks and the realization of the classical simulated annealing (SA) algorithm. Its weakness is also discussed and an enhanced SA algorithm is then proposed. The new algorithm tackles the global optimization and the local optimization processes separately. With an enhanced non-uniform mutation method, the global search range is expanded, which also leads to an improved local optimal solution. Finally, this paper uses a real-world optimization problem to contrast the conventional and the enhanced SA algorithms, and demonstrates the superiority of the newly proposed technique.

DOI 10.11648/j.sd.20160401.19
Published in Science Discovery (Volume 4, Issue 1, February 2016)
Page(s) 52-55
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Simulated Annealing Algorithm, Combinatorial Optimization, Gauss Distribution, Metropolis Sampling, Global Optimization

References
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Author Information
  • School of Internet of Things (IoT) Engineering, Jiangnan University, Wuxi, China

  • School of Internet of Things (IoT) Engineering, Jiangnan University, Wuxi, China

  • School of Internet of Things (IoT) Engineering, Jiangnan University, Wuxi, China

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    Sang Jie, Zhan Hu, Song Chun-lin. (2016). Simulated Annealing Algorithm Based on Gauss Distribution. Science Discovery, 4(1), 52-55. https://doi.org/10.11648/j.sd.20160401.19

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    ACS Style

    Sang Jie; Zhan Hu; Song Chun-lin. Simulated Annealing Algorithm Based on Gauss Distribution. Sci. Discov. 2016, 4(1), 52-55. doi: 10.11648/j.sd.20160401.19

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    AMA Style

    Sang Jie, Zhan Hu, Song Chun-lin. Simulated Annealing Algorithm Based on Gauss Distribution. Sci Discov. 2016;4(1):52-55. doi: 10.11648/j.sd.20160401.19

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  • @article{10.11648/j.sd.20160401.19,
      author = {Sang Jie and Zhan Hu and Song Chun-lin},
      title = {Simulated Annealing Algorithm Based on Gauss Distribution},
      journal = {Science Discovery},
      volume = {4},
      number = {1},
      pages = {52-55},
      doi = {10.11648/j.sd.20160401.19},
      url = {https://doi.org/10.11648/j.sd.20160401.19},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.sd.20160401.19},
      abstract = {This paper first describes in a brief manner the principle, the building blocks and the realization of the classical simulated annealing (SA) algorithm. Its weakness is also discussed and an enhanced SA algorithm is then proposed. The new algorithm tackles the global optimization and the local optimization processes separately. With an enhanced non-uniform mutation method, the global search range is expanded, which also leads to an improved local optimal solution. Finally, this paper uses a real-world optimization problem to contrast the conventional and the enhanced SA algorithms, and demonstrates the superiority of the newly proposed technique.},
     year = {2016}
    }
    

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  • TY  - JOUR
    T1  - Simulated Annealing Algorithm Based on Gauss Distribution
    AU  - Sang Jie
    AU  - Zhan Hu
    AU  - Song Chun-lin
    Y1  - 2016/04/18
    PY  - 2016
    N1  - https://doi.org/10.11648/j.sd.20160401.19
    DO  - 10.11648/j.sd.20160401.19
    T2  - Science Discovery
    JF  - Science Discovery
    JO  - Science Discovery
    SP  - 52
    EP  - 55
    PB  - Science Publishing Group
    SN  - 2331-0650
    UR  - https://doi.org/10.11648/j.sd.20160401.19
    AB  - This paper first describes in a brief manner the principle, the building blocks and the realization of the classical simulated annealing (SA) algorithm. Its weakness is also discussed and an enhanced SA algorithm is then proposed. The new algorithm tackles the global optimization and the local optimization processes separately. With an enhanced non-uniform mutation method, the global search range is expanded, which also leads to an improved local optimal solution. Finally, this paper uses a real-world optimization problem to contrast the conventional and the enhanced SA algorithms, and demonstrates the superiority of the newly proposed technique.
    VL  - 4
    IS  - 1
    ER  - 

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