Simulated Annealing Algorithm Based on Gauss Distribution

Sang Jie; Zhan Hu; Song Chun-lin

doi:doi:10.11648/j.sd.20160401.19

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Simulated Annealing Algorithm Based on Gauss Distribution

Sang Jie, Zhan Hu, Song Chun-lin

Published in Science Discovery (Volume 4, Issue 1)

Received: 17 April 2016 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.

Published in	Science Discovery (Volume 4, Issue 1)
DOI	10.11648/j.sd.20160401.19
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), 2016. Published by Science Publishing Group

Keywords

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

References

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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://article.sciencepublishinggroup.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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Author Information

Sang Jie

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

School of Internet of Things (IoT) Engineering, Jiangnan University, Wuxi, China
Song Chun-lin

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

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APA Style

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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://article.sciencepublishinggroup.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}
}

Copy | Download

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  -

Copy | Download