Multi-objective Optimization of Cloud Manufacturing Service Composition with Cloud-Entropy Enhanced Genetic Algorithm

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LI, Yongxiang ;YAO, Xifan ;ZHOU, Jifeng .
Multi-objective Optimization of Cloud Manufacturing Service Composition with Cloud-Entropy Enhanced Genetic Algorithm. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 62, n.10, p. 577-590, june 2018. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/article/multi-objective-optimization-of-cloud-manufacturing-service-composition-with-cloud-entropy-enhanced-genetic-algorithm/>. Date accessed: 06 aug. 2020. 
doi:http://dx.doi.org/10.5545/sv-jme.2016.3545.
Li, Y., Yao, X., & Zhou, J.
(2016).
Multi-objective Optimization of Cloud Manufacturing Service Composition with Cloud-Entropy Enhanced Genetic Algorithm.
Strojniški vestnik - Journal of Mechanical Engineering, 62(10), 577-590.
doi:http://dx.doi.org/10.5545/sv-jme.2016.3545
@article{sv-jmesv-jme.2016.3545,
	author = {Yongxiang  Li and Xifan  Yao and Jifeng  Zhou},
	title = {Multi-objective Optimization of Cloud Manufacturing Service Composition with Cloud-Entropy Enhanced Genetic Algorithm},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {62},
	number = {10},
	year = {2016},
	keywords = {cloud manufacturing; service composition optimization; cloud-entropy; service matching degree; composition harmony degree; genetic algorithm},
	abstract = {To consider the service-matching degree, the composition harmony degree, and the service composition complexity in cloud manufacturing service composition optimization problems, a new composition optimization approach, called cloud-entropy enhanced genetic algorithm (CEGA), is put forward to solve such problems with multi-objectives. The definitions of service-matching degree, composition harmony degree, and cloud-entropy and the corresponding calculation methods are given. A multi-objective optimization mathematical model of cloud manufacturing service composition is built. The manufacturing task of AGV (automated guided vehicle) is taken as an example to verify the proposed CEGA algorithm on the established composition model. The studied result shows that CEGA converges faster than a standard genetic algorithm with shorter time.},
	issn = {0039-2480},	pages = {577-590},	doi = {10.5545/sv-jme.2016.3545},
	url = {https://www.sv-jme.eu/article/multi-objective-optimization-of-cloud-manufacturing-service-composition-with-cloud-entropy-enhanced-genetic-algorithm/}
}
Li, Y.,Yao, X.,Zhou, J.
2016 June 62. Multi-objective Optimization of Cloud Manufacturing Service Composition with Cloud-Entropy Enhanced Genetic Algorithm. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 62:10
%A Li, Yongxiang 
%A Yao, Xifan 
%A Zhou, Jifeng 
%D 2016
%T Multi-objective Optimization of Cloud Manufacturing Service Composition with Cloud-Entropy Enhanced Genetic Algorithm
%B 2016
%9 cloud manufacturing; service composition optimization; cloud-entropy; service matching degree; composition harmony degree; genetic algorithm
%! Multi-objective Optimization of Cloud Manufacturing Service Composition with Cloud-Entropy Enhanced Genetic Algorithm
%K cloud manufacturing; service composition optimization; cloud-entropy; service matching degree; composition harmony degree; genetic algorithm
%X To consider the service-matching degree, the composition harmony degree, and the service composition complexity in cloud manufacturing service composition optimization problems, a new composition optimization approach, called cloud-entropy enhanced genetic algorithm (CEGA), is put forward to solve such problems with multi-objectives. The definitions of service-matching degree, composition harmony degree, and cloud-entropy and the corresponding calculation methods are given. A multi-objective optimization mathematical model of cloud manufacturing service composition is built. The manufacturing task of AGV (automated guided vehicle) is taken as an example to verify the proposed CEGA algorithm on the established composition model. The studied result shows that CEGA converges faster than a standard genetic algorithm with shorter time.
%U https://www.sv-jme.eu/article/multi-objective-optimization-of-cloud-manufacturing-service-composition-with-cloud-entropy-enhanced-genetic-algorithm/
%0 Journal Article
%R 10.5545/sv-jme.2016.3545
%& 577
%P 14
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 62
%N 10
%@ 0039-2480
%8 2018-06-27
%7 2018-06-27
Li, Yongxiang, Xifan  Yao, & Jifeng  Zhou.
"Multi-objective Optimization of Cloud Manufacturing Service Composition with Cloud-Entropy Enhanced Genetic Algorithm." Strojniški vestnik - Journal of Mechanical Engineering [Online], 62.10 (2016): 577-590. Web.  06 Aug. 2020
TY  - JOUR
AU  - Li, Yongxiang 
AU  - Yao, Xifan 
AU  - Zhou, Jifeng 
PY  - 2016
TI  - Multi-objective Optimization of Cloud Manufacturing Service Composition with Cloud-Entropy Enhanced Genetic Algorithm
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2016.3545
KW  - cloud manufacturing; service composition optimization; cloud-entropy; service matching degree; composition harmony degree; genetic algorithm
N2  - To consider the service-matching degree, the composition harmony degree, and the service composition complexity in cloud manufacturing service composition optimization problems, a new composition optimization approach, called cloud-entropy enhanced genetic algorithm (CEGA), is put forward to solve such problems with multi-objectives. The definitions of service-matching degree, composition harmony degree, and cloud-entropy and the corresponding calculation methods are given. A multi-objective optimization mathematical model of cloud manufacturing service composition is built. The manufacturing task of AGV (automated guided vehicle) is taken as an example to verify the proposed CEGA algorithm on the established composition model. The studied result shows that CEGA converges faster than a standard genetic algorithm with shorter time.
UR  - https://www.sv-jme.eu/article/multi-objective-optimization-of-cloud-manufacturing-service-composition-with-cloud-entropy-enhanced-genetic-algorithm/
@article{{sv-jme}{sv-jme.2016.3545},
	author = {Li, Y., Yao, X., Zhou, J.},
	title = {Multi-objective Optimization of Cloud Manufacturing Service Composition with Cloud-Entropy Enhanced Genetic Algorithm},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {62},
	number = {10},
	year = {2016},
	doi = {10.5545/sv-jme.2016.3545},
	url = {https://www.sv-jme.eu/article/multi-objective-optimization-of-cloud-manufacturing-service-composition-with-cloud-entropy-enhanced-genetic-algorithm/}
}
TY  - JOUR
AU  - Li, Yongxiang 
AU  - Yao, Xifan 
AU  - Zhou, Jifeng 
PY  - 2018/06/27
TI  - Multi-objective Optimization of Cloud Manufacturing Service Composition with Cloud-Entropy Enhanced Genetic Algorithm
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 62, No 10 (2016): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2016.3545
KW  - cloud manufacturing, service composition optimization, cloud-entropy, service matching degree, composition harmony degree, genetic algorithm
N2  - To consider the service-matching degree, the composition harmony degree, and the service composition complexity in cloud manufacturing service composition optimization problems, a new composition optimization approach, called cloud-entropy enhanced genetic algorithm (CEGA), is put forward to solve such problems with multi-objectives. The definitions of service-matching degree, composition harmony degree, and cloud-entropy and the corresponding calculation methods are given. A multi-objective optimization mathematical model of cloud manufacturing service composition is built. The manufacturing task of AGV (automated guided vehicle) is taken as an example to verify the proposed CEGA algorithm on the established composition model. The studied result shows that CEGA converges faster than a standard genetic algorithm with shorter time.
UR  - https://www.sv-jme.eu/article/multi-objective-optimization-of-cloud-manufacturing-service-composition-with-cloud-entropy-enhanced-genetic-algorithm/
Li, Yongxiang, Yao, Xifan, AND Zhou, Jifeng.
"Multi-objective Optimization of Cloud Manufacturing Service Composition with Cloud-Entropy Enhanced Genetic Algorithm" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 62 Number 10 (27 June 2018)

Authors

Affiliations

  • South China University of Technology, School of Mechanical & Automotive Engineering, 1

Paper's information

Strojniški vestnik - Journal of Mechanical Engineering 62(2016)10, 577-590

https://doi.org/10.5545/sv-jme.2016.3545

To consider the service-matching degree, the composition harmony degree, and the service composition complexity in cloud manufacturing service composition optimization problems, a new composition optimization approach, called cloud-entropy enhanced genetic algorithm (CEGA), is put forward to solve such problems with multi-objectives. The definitions of service-matching degree, composition harmony degree, and cloud-entropy and the corresponding calculation methods are given. A multi-objective optimization mathematical model of cloud manufacturing service composition is built. The manufacturing task of AGV (automated guided vehicle) is taken as an example to verify the proposed CEGA algorithm on the established composition model. The studied result shows that CEGA converges faster than a standard genetic algorithm with shorter time.

cloud manufacturing; service composition optimization; cloud-entropy; service matching degree; composition harmony degree; genetic algorithm