Cloud Service Optimization Method Based on Dynamic Artificial Ant-Bee Colony Algorithm in Agricultural Equipment Manufacturing

In view of the miniaturization and decentralization characteristics of agricultural equipment factories in China, agricultural equipment manufacturing is well suited to the cloud manufacturing model, but there is no specific research on cloud services optimization for it. To fill the research gap, a...

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Bibliographic Details
Published in:Mathematical problems in engineering 2020, Vol.2020 (2020), p.1-11
Main Authors: Jin, Tianguo, Ni, Zhiyong, Wu, Wanying, Wen, Yongzhao, Zhou, Kai, Long, Xiaojun
Format: Article
Language:English
Subjects:
Accuracy
Agricultural equipment
Agricultural production
Ant colony optimization
Cloud computing
Decomposition
Efficiency
Factories
Farming
Feedback
Genetic algorithms
Industrial plants
Iterative methods
Manufacturing
Mathematical models
Miniaturization
Optimization
Quality of service
Search algorithms
Strategy
Swarm intelligence
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Summary:In view of the miniaturization and decentralization characteristics of agricultural equipment factories in China, agricultural equipment manufacturing is well suited to the cloud manufacturing model, but there is no specific research on cloud services optimization for it. To fill the research gap, a cloud service optimization method is proposed in this paper. For the optimization model, the dynamic coefficient strategy and the reliability feedback update strategy are added to the mathematical model to strengthen the applicability of farming season. As optimization algorithm, a dynamic artificial ant-bee colony algorithm (DAABA) based on artificial ant colony algorithm and bee colony algorithm is presented. The optimal fusion evaluation strategy is used to save optimization time by reducing the useless iteration, and the iterative adjustment threshold strategy is adopted to improve the accuracy of cloud service by increasing the size of bee colony. Finally, the performance of DAABA is verified to be more superior by comparing with other algorithms.
ISSN:1024-123X
1563-5147
DOI:10.1155/2020/9134695