The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
L'optimisation de la composition des services est un problème NP-difficile classique. Comment sélectionner rapidement des services de haute qualité qui répondent aux besoins des utilisateurs parmi un grand nombre de services candidats est un sujet brûlant dans la recherche sur la composition des services cloud. Une optimisation efficace des essaims de coléoptères de second ordre est proposée avec une capacité de recherche globale pour résoudre le problème de l'optimisation de la composition des services cloud dans cette étude. Tout d'abord, l'algorithme de recherche d'antennes de coléoptères est introduit dans l'algorithme d'optimisation d'essaim de particules modifié, initialise la population en utilisant une séquence chaotique, et les facteurs d'apprentissage trigonométriques dynamiques non linéaires modifiés sont adoptés pour contrôler la capacité d'expansion des particules et la capacité de convergence globale. Deuxièmement, des facteurs d'oscillation secondaires modifiés sont incorporés, augmentant ainsi la précision de recherche de l'algorithme et la capacité de recherche globale. Un ajustement adaptatif par étapes est utilisé pour améliorer la stabilité de l'algorithme. Les résultats expérimentaux fondés sur un ensemble de données réelles ont indiqué que l'algorithme d'optimisation globale proposé peut résoudre les problèmes d'optimisation de la composition des services Web dans un environnement cloud. Il présente une excellente capacité de recherche globale, une vitesse de convergence relativement rapide, une stabilité favorable et nécessite moins de temps.
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
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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},}
Copier
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 -