IEICE TRANSACTIONS on Information
Global Optimization Algorithm for Cloud Service Composition
Summary :
Service composition optimization is a classic NP-hard problem. How to quickly select high-quality services that meet user needs from a large number of candidate services is a hot topic in cloud service composition research. An efficient second-order beetle swarm optimization is proposed with a global search ability to solve the problem of cloud service composition optimization in this study. First, the beetle antennae search algorithm is introduced into the modified particle swarm optimization algorithm, initialize the population bying using a chaotic sequence, and the modified nonlinear dynamic trigonometric learning factors are adopted to control the expanding capacity of particles and global convergence capability. Second, modified secondary oscillation factors are incorporated, increasing the search precision of the algorithm and global searching ability. An adaptive step adjustment is utilized to improve the stability of the algorithm. Experimental results founded on a real data set indicated that the proposed global optimization algorithm can solve web service composition optimization problems in a cloud environment. It exhibits excellent global searching ability, has comparatively fast convergence speed, favorable stability, and requires less time cost.
- Publication
- IEICE TRANSACTIONS on Information Vol.E104-D No.10 pp.1580-1591
- Publication Date
- 2021/10/01
- Publicized
- 2021/06/30
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2020EDP7233
- Type of Manuscript
- PAPER
- Category
- Computer System
Authors
Hongwei YANG
Changchun University of Science and Technology
Fucheng XUE
Changchun University of Science and Technology
Dan LIU
Changchun University of Science and Technology
Li LI
Changchun University of Science and Technology
Jiahui FENG
Changchun University of Science and Technology
Keyword
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Hongwei YANG, Fucheng XUE, Dan LIU, Li LI, Jiahui FENG, "Global Optimization Algorithm for Cloud Service Composition" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 10, pp. 1580-1591, October 2021, doi: 10.1587/transinf.2020EDP7233.
Abstract: Service composition optimization is a classic NP-hard problem. How to quickly select high-quality services that meet user needs from a large number of candidate services is a hot topic in cloud service composition research. An efficient second-order beetle swarm optimization is proposed with a global search ability to solve the problem of cloud service composition optimization in this study. First, the beetle antennae search algorithm is introduced into the modified particle swarm optimization algorithm, initialize the population bying using a chaotic sequence, and the modified nonlinear dynamic trigonometric learning factors are adopted to control the expanding capacity of particles and global convergence capability. Second, modified secondary oscillation factors are incorporated, increasing the search precision of the algorithm and global searching ability. An adaptive step adjustment is utilized to improve the stability of the algorithm. Experimental results founded on a real data set indicated that the proposed global optimization algorithm can solve web service composition optimization problems in a cloud environment. It exhibits excellent global searching ability, has comparatively fast convergence speed, favorable stability, and requires less time cost.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDP7233/_p
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@ARTICLE{e104-d_10_1580,
author={Hongwei YANG, Fucheng XUE, Dan LIU, Li LI, Jiahui FENG, },
journal={IEICE TRANSACTIONS on Information},
title={Global Optimization Algorithm for Cloud Service Composition},
year={2021},
volume={E104-D},
number={10},
pages={1580-1591},
abstract={Service composition optimization is a classic NP-hard problem. How to quickly select high-quality services that meet user needs from a large number of candidate services is a hot topic in cloud service composition research. An efficient second-order beetle swarm optimization is proposed with a global search ability to solve the problem of cloud service composition optimization in this study. First, the beetle antennae search algorithm is introduced into the modified particle swarm optimization algorithm, initialize the population bying using a chaotic sequence, and the modified nonlinear dynamic trigonometric learning factors are adopted to control the expanding capacity of particles and global convergence capability. Second, modified secondary oscillation factors are incorporated, increasing the search precision of the algorithm and global searching ability. An adaptive step adjustment is utilized to improve the stability of the algorithm. Experimental results founded on a real data set indicated that the proposed global optimization algorithm can solve web service composition optimization problems in a cloud environment. It exhibits excellent global searching ability, has comparatively fast convergence speed, favorable stability, and requires less time cost.},
keywords={},
doi={10.1587/transinf.2020EDP7233},
ISSN={1745-1361},
month={October},}
Copy
TY - JOUR
TI - Global Optimization Algorithm for Cloud Service Composition
T2 - IEICE TRANSACTIONS on Information
SP - 1580
EP - 1591
AU - Hongwei YANG
AU - Fucheng XUE
AU - Dan LIU
AU - Li LI
AU - Jiahui FENG
PY - 2021
DO - 10.1587/transinf.2020EDP7233
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2021
AB - Service composition optimization is a classic NP-hard problem. How to quickly select high-quality services that meet user needs from a large number of candidate services is a hot topic in cloud service composition research. An efficient second-order beetle swarm optimization is proposed with a global search ability to solve the problem of cloud service composition optimization in this study. First, the beetle antennae search algorithm is introduced into the modified particle swarm optimization algorithm, initialize the population bying using a chaotic sequence, and the modified nonlinear dynamic trigonometric learning factors are adopted to control the expanding capacity of particles and global convergence capability. Second, modified secondary oscillation factors are incorporated, increasing the search precision of the algorithm and global searching ability. An adaptive step adjustment is utilized to improve the stability of the algorithm. Experimental results founded on a real data set indicated that the proposed global optimization algorithm can solve web service composition optimization problems in a cloud environment. It exhibits excellent global searching ability, has comparatively fast convergence speed, favorable stability, and requires less time cost.
ER -
English
