IEICE TRANSACTIONS on Information
Cloud Annealing: A Novel Simulated Annealing Algorithm Based on Cloud Model
Summary :
As one of the most popular intelligent optimization algorithms, Simulated Annealing (SA) faces two key problems, the generation of perturbation solutions and the control strategy of the outer loop (cooling schedule). In this paper, we introduce the Gaussian Cloud model to solve both problems and propose a novel cloud annealing algorithm. Its basic idea is to use the Gaussian Cloud model with decreasing numerical character He (Hyper-entropy) to generate new solutions in the inner loop, while He essentially indicates a heuristic control strategy to combine global random search of the outer loop and local tuning search of the inner loop. Experimental results in function optimization problems (i.e. single-peak, multi-peak and high dimensional functions) show that, compared with the simple SA algorithm, the proposed cloud annealing algorithm will lead to significant improvement on convergence and the average value of obtained solutions is usually closer to the optimal solution.
- Publication
- IEICE TRANSACTIONS on Information Vol.E103-D No.1 pp.85-92
- Publication Date
- 2020/01/01
- Publicized
- 2019/09/27
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2019EDP7059
- Type of Manuscript
- PAPER
- Category
- Fundamentals of Information Systems
Authors
Shanshan JIAO
Army Engineering University of PLA
Zhisong PAN
Army Engineering University of PLA
Yutian CHEN
Army Engineering University of PLA
Yunbo LI
Army Engineering University of PLA
Keyword
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Shanshan JIAO, Zhisong PAN, Yutian CHEN, Yunbo LI, "Cloud Annealing: A Novel Simulated Annealing Algorithm Based on Cloud Model" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 1, pp. 85-92, January 2020, doi: 10.1587/transinf.2019EDP7059.
Abstract: As one of the most popular intelligent optimization algorithms, Simulated Annealing (SA) faces two key problems, the generation of perturbation solutions and the control strategy of the outer loop (cooling schedule). In this paper, we introduce the Gaussian Cloud model to solve both problems and propose a novel cloud annealing algorithm. Its basic idea is to use the Gaussian Cloud model with decreasing numerical character He (Hyper-entropy) to generate new solutions in the inner loop, while He essentially indicates a heuristic control strategy to combine global random search of the outer loop and local tuning search of the inner loop. Experimental results in function optimization problems (i.e. single-peak, multi-peak and high dimensional functions) show that, compared with the simple SA algorithm, the proposed cloud annealing algorithm will lead to significant improvement on convergence and the average value of obtained solutions is usually closer to the optimal solution.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDP7059/_p
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@ARTICLE{e103-d_1_85,
author={Shanshan JIAO, Zhisong PAN, Yutian CHEN, Yunbo LI, },
journal={IEICE TRANSACTIONS on Information},
title={Cloud Annealing: A Novel Simulated Annealing Algorithm Based on Cloud Model},
year={2020},
volume={E103-D},
number={1},
pages={85-92},
abstract={As one of the most popular intelligent optimization algorithms, Simulated Annealing (SA) faces two key problems, the generation of perturbation solutions and the control strategy of the outer loop (cooling schedule). In this paper, we introduce the Gaussian Cloud model to solve both problems and propose a novel cloud annealing algorithm. Its basic idea is to use the Gaussian Cloud model with decreasing numerical character He (Hyper-entropy) to generate new solutions in the inner loop, while He essentially indicates a heuristic control strategy to combine global random search of the outer loop and local tuning search of the inner loop. Experimental results in function optimization problems (i.e. single-peak, multi-peak and high dimensional functions) show that, compared with the simple SA algorithm, the proposed cloud annealing algorithm will lead to significant improvement on convergence and the average value of obtained solutions is usually closer to the optimal solution.},
keywords={},
doi={10.1587/transinf.2019EDP7059},
ISSN={1745-1361},
month={January},}
Copy
TY - JOUR
TI - Cloud Annealing: A Novel Simulated Annealing Algorithm Based on Cloud Model
T2 - IEICE TRANSACTIONS on Information
SP - 85
EP - 92
AU - Shanshan JIAO
AU - Zhisong PAN
AU - Yutian CHEN
AU - Yunbo LI
PY - 2020
DO - 10.1587/transinf.2019EDP7059
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2020
AB - As one of the most popular intelligent optimization algorithms, Simulated Annealing (SA) faces two key problems, the generation of perturbation solutions and the control strategy of the outer loop (cooling schedule). In this paper, we introduce the Gaussian Cloud model to solve both problems and propose a novel cloud annealing algorithm. Its basic idea is to use the Gaussian Cloud model with decreasing numerical character He (Hyper-entropy) to generate new solutions in the inner loop, while He essentially indicates a heuristic control strategy to combine global random search of the outer loop and local tuning search of the inner loop. Experimental results in function optimization problems (i.e. single-peak, multi-peak and high dimensional functions) show that, compared with the simple SA algorithm, the proposed cloud annealing algorithm will lead to significant improvement on convergence and the average value of obtained solutions is usually closer to the optimal solution.
ER -
English
