An adaptive robust service composition and optimal selection method for cloud manufacturing based on the enhanced multi-objective artificial hummingbird algorithm

Author:

Zhang QianfuORCID,Li Shaobo,Pu Ruiqiang,Zhou Peng,Chen Guanglin,Li Kaixin,Lv Dongchao

Funder

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference58 articles.

1. Multi-objective architecture for strategic integration of distributed energy resources and battery storage system in microgrids;Abid;Journal of Energy Storage,2023

2. A novel model for optimisation of logistics and manufacturing operation service composition in Cloud manufacturing system focusing on cloud-entropy;Aghamohammadzadeh;International Journal of Production Research,2020

3. Integrated configuration design and capacity planning in a dynamic cloud manufacturing system;Arbabi;International Journal of Production Research,2023

4. Babu, B., & Anbarasu, B. (2005). Multi-objective differential evolution (MODE): An evolutionary algorithm for multi-objective optimization problems (MOOPs). Proceedings of international symposium and 58th annual session of IIChE.

5. Using machine learning for service candidate sets retrieval in service composition of cloud-based manufacturing;Bouzary;The International Journal of Advanced Manufacturing Technology,2021

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