Juan Li
Orcid: 0000-0002-9836-5125Affiliations:
- Beijing Institute of Technology, School of Automation, China
According to our database1,
Juan Li
authored at least 12 papers
between 2015 and 2021.
Collaborative distances:
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Bibliography
2021
A Hybrid Decomposition-based Multi-objective Evolutionary Algorithm for the Multi-Point Dynamic Aggregation Problem.
CoRR, 2021
Solving bi-objective uncertain stochastic resource allocation problems by the CVaR-based risk measure and decomposition-based multi-objective evolutionary algorithms.
Ann. Oper. Res., 2021
2020
Noise-Tolerant Techniques for Decomposition-Based Multiobjective Evolutionary Algorithms.
IEEE Trans. Cybern., 2020
DMaOEA-<i>ε</i>C: Decomposition-based many-objective evolutionary algorithm with the <i>ε</i>-constraint framework.
Inf. Sci., 2020
WorldScientific, ISBN: 9789811219009, 2020
2019
The bi-objective critical node detection problem with minimum pairwise connectivity and cost: theory and algorithms.
Soft Comput., 2019
Optimizing multi-objective uncertain multi-stage weapon target assignment problems with the risk measure CVaR.
Proceedings of the 15th IEEE International Conference on Control and Automation, 2019
2017
DMOEA-εC: Decomposition-Based Multiobjective Evolutionary Algorithm With the ε-Constraint Framework.
IEEE Trans. Evol. Comput., 2017
Efficient multi-objective evolutionary algorithms for solving the multi-stage weapon target assignment problem: A comparison study.
Proceedings of the 2017 IEEE Congress on Evolutionary Computation, 2017
A virtual-decision-maker library considering personalities and dynamically changing preference structures for interactive multiobjective optimization.
Proceedings of the 2017 IEEE Congress on Evolutionary Computation, 2017
2016
Solving the uncertain multi-objective multi-stage weapon target assignment problem via MOEA/D-AWA.
Proceedings of the IEEE Congress on Evolutionary Computation, 2016
2015
Solving multi-objective multi-stage weapon target assignment problem via adaptive NSGAII and adaptive MOEA/D: A comparison study.
Proceedings of the IEEE Congress on Evolutionary Computation, 2015