Multi-Agent Interests Service Composition Optimization in Cloud Manufacturing Environment
Author:
Affiliation:
1. School of Business, Henan University of Science and Technology, Luoyang, China
2. First Affiliated Hospital, Henan University of Science and Technology, Luoyang, China
Funder
National Natural Science Foundation of China
Henan Province Soft Science Research Project: Construction and Management Countermeasures of Henan Technological Innovation Center in the New Era
Henan Province Soft Science Research Project: Evaluation and Countermeasures of Technology Transfer Status in Henan Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10130163.pdf?arnumber=10130163
Reference42 articles.
1. An enhanced multi-objective grey wolf optimizer for service composition in cloud manufacturing
2. Eagle strategy using uniform mutation and modified whale optimization algorithm for QoS-aware cloud service composition
3. Improved adaptive immune genetic algorithm for optimal QoS-aware service composition selection in cloud manufacturing
4. An efficient two-phase approach for reliable collaboration-aware service composition in cloud manufacturing
5. Manufacturing service reliability assessment in cloud manufacturing
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1. Bi-level optimization of shared manufacturing service composition based on improved NSGA-II;PLOS ONE;2024-06-17
2. An adaptive robust service composition and optimal selection method for cloud manufacturing based on the enhanced multi-objective artificial hummingbird algorithm;Expert Systems with Applications;2024-06
3. An improved ACO based service composition algorithm in multi-cloud networks;Journal of Cloud Computing;2024-01-15
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