Journal
2017 IEEE 6TH INTERNATIONAL CONGRESS ON BIG DATA (BIGDATA CONGRESS 2017)
Volume -, Issue -, Pages 577-581Publisher
IEEE
DOI: 10.1109/BigDataCongress.2017.88
Keywords
big data driven; evolutionary computation; cutting parameters optimization; teaching and learning algorithm
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Funding
- National Natural Science Foundation of China [51475150]
- Hubei Natural Science Foundation of China [D2016CFB402, D2013CFB045]
- Key project of Hubei Provincial Education Department [D20141802]
- Hubei Provincial Department of Education Instructive Foundation China [B2016084]
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At present, it is an important method to solve the problem of industrial multi-objective optimization based on data-driven evolutionary, optimization. In this paper, we study the multi-objective optimization problem of CNC cutting based on data-driven teaching and learning based optimization algorithm. Based on the large-scale off-line CNC cutting data to establish the optimization model, using the improved teaching and learning based optimization algorithm (ITLBO) to solve The simulation experimental results show that the optimization results are better.
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