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Cloud Manufacturing Service Composition Optimization with Improved Genetic Algorithm
المؤلفون المشاركون
Yao, Xifan
Li, Yongxiang
Liu, Min
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-19، 19ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-07-03
دولة النشر
مصر
عدد الصفحات
19
التخصصات الرئيسية
الملخص EN
Aiming at the problems in which there exists collocation between services and manufacturing tasks, multiobjective cloud manufacturing service composition optimization seldom considers the synergy degree of composite cloud services and the complexity of service composition, so a novel service composition optimization approach, called improved genetic algorithm based on entropy (IGABE), is put forward.
First, the mathematical expressions of service collocation degree, composition synergy degree, composition entropy, and their related influence factors of the service composition are analyzed, and their definitions and calculation methods are given.
Then, a multiobjective cloud manufacturing service composition optimization mathematical model is established.
Moreover, crossover and mutation operators are improved by introducing normal cloud model theory and piecewise function, and improved roulette selection method is used to perform the selection operation.
And the fitness function of the proposed IGABE is designed by combining Euclidean deviation with angular deviation.
Finally, the manufacturing task of a wheeled cleaning robot is exemplified to verify the correctness of the proposed multiobjective optimization model for cloud manufacturing service composition and the effectiveness of the proposed algorithm, compared with Standard Genetic Algorithm (SGA), Hybrid Genetic Algorithm (HGA), and Cloud-entropy Enhanced Genetic Algorithm (CEGA).
The studied results show that IGABE converges faster than SGA and HGA and can analyze and reflect the content difference expressed by the objective functions of service composition scheme and its approximation degree to the corresponding dimensions of the ideal point vector more comprehensively than CEGA.
As such, the optimal service composition obtained by IGABE algorithm can better meet the complex needs of users.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Li, Yongxiang& Yao, Xifan& Liu, Min. 2019. Cloud Manufacturing Service Composition Optimization with Improved Genetic Algorithm. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-19.
https://search.emarefa.net/detail/BIM-1196862
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Li, Yongxiang…[et al.]. Cloud Manufacturing Service Composition Optimization with Improved Genetic Algorithm. Mathematical Problems in Engineering No. 2019 (2019), pp.1-19.
https://search.emarefa.net/detail/BIM-1196862
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Li, Yongxiang& Yao, Xifan& Liu, Min. Cloud Manufacturing Service Composition Optimization with Improved Genetic Algorithm. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-19.
https://search.emarefa.net/detail/BIM-1196862
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1196862
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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