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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Pengoptimuman komposisi perkhidmatan ialah masalah NP-hard klasik. Cara cepat memilih perkhidmatan berkualiti tinggi yang memenuhi keperluan pengguna daripada sejumlah besar perkhidmatan calon adalah topik hangat dalam penyelidikan komposisi perkhidmatan awan. Pengoptimuman kumpulan kumbang peringkat kedua yang cekap dicadangkan dengan keupayaan carian global untuk menyelesaikan masalah pengoptimuman komposisi perkhidmatan awan dalam kajian ini. Pertama, algoritma carian antena kumbang diperkenalkan ke dalam algoritma pengoptimuman kawanan zarah yang diubah suai, memulakan populasi dengan menggunakan urutan huru-hara, dan faktor pembelajaran trigonometri dinamik tak linear yang diubah suai digunakan untuk mengawal kapasiti pengembangan zarah dan keupayaan penumpuan global. Kedua, faktor ayunan sekunder yang diubah suai digabungkan, meningkatkan ketepatan carian algoritma dan keupayaan carian global. Pelarasan langkah penyesuaian digunakan untuk meningkatkan kestabilan algoritma. Keputusan eksperimen yang diasaskan pada set data sebenar menunjukkan bahawa algoritma pengoptimuman global yang dicadangkan boleh menyelesaikan masalah pengoptimuman komposisi perkhidmatan web dalam persekitaran awan. Ia mempamerkan keupayaan pencarian global yang sangat baik, mempunyai kelajuan penumpuan yang agak pantas, kestabilan yang menggalakkan, dan memerlukan kos masa yang lebih sedikit.
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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Salinan
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
Salinan
@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},}
Salinan
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 -