Simulated Annealing Algorithm Based on Gauss Distribution
Science Discovery
Volume 4, Issue 1, February 2016, Pages: 52-55
Received: Apr. 17, 2016; Published: Apr. 18, 2016
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Authors
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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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.
Keywords
Simulated Annealing Algorithm, Combinatorial Optimization, Gauss Distribution, Metropolis Sampling, Global Optimization
To cite this article
Sang Jie, Zhan Hu, Song Chun-lin, Simulated Annealing Algorithm Based on Gauss Distribution, Science Discovery. Vol. 4, No. 1, 2016, pp. 52-55. doi: 10.11648/j.sd.20160401.19
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