Funder
Science, Technology and Innovation Commission of Shenzhen Municipality
National Key Research and Development Program of China
National Natural Science Foundation of China
Natural Science Foundation of Guangdong Province
Reference66 articles.
1. Ahrari, A., Elsayed, S., Sarker, R., & Essam, D. (2019). A New Prediction Approach for Dynamic Multiobjective Optimization. In Proceedings of the IEEE congress on evolutionary computation, CEC 2019, Wellington,New Zealand (pp. 2268–2275).
2. Solving multiobjective optimization problems in unknown dynamic environments: An inverse modeling approach;Bong;IEEE Transactions on Cybernetics,2017
3. Continuous dynamic constrained optimization with ensemble of locating and tracking feasible regions strategies;Bu;IEEE Transactions on Evolutionary Computation,2017
4. A novel evolutionary algorithm for dynamic constrained multiobjective optimization problems;Chen;IEEE Transactions on Evolutionary Computation,2020
5. A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem;Chen;Computers & Industrial Engineering,2020
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