H
Hui Li
Researcher at Beihang University
Publications - 99
Citations - 14278
Hui Li is an academic researcher from Beihang University. The author has contributed to research in topics: Evolutionary algorithm & Multi-objective optimization. The author has an hindex of 27, co-authored 81 publications receiving 11049 citations. Previous affiliations of Hui Li include University of Nottingham & University of Essex.
Papers
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Journal ArticleDOI
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Qingfu Zhang,Hui Li +1 more
TL;DR: Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjectives optimization problems.
Journal ArticleDOI
Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II
Hui Li,Qingfu Zhang +1 more
TL;DR: The experimental results indicate that MOEA/D could significantly outperform NSGA-II on these test instances, and suggests that decomposition based multiobjective evolutionary algorithms are very promising in dealing with complicated PS shapes.
Journal ArticleDOI
Multiobjective evolutionary algorithms: A survey of the state of the art
TL;DR: This paper surveys the development ofMOEAs primarily during the last eight years and covers algorithmic frameworks such as decomposition-based MOEAs (MOEA/Ds), memetic MOEas, coevolutionary MOE As, selection and offspring reproduction operators, MOE as with specific search methods, MOeAs for multimodal problems, constraint handling and MOE
Proceedings ArticleDOI
The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances
Qingfu Zhang,Wudong Liu,Hui Li +2 more
TL;DR: The new version of MOEA/D has been tested on all the CEC09 unconstrained MOP test instances and a strategy for allocating the computational resource to different subproblems in MOEA /D is proposed.
Proceedings ArticleDOI
Opposition-based particle swarm algorithm with cauchy mutation
TL;DR: An Opposition-based PSO (OPSO) to accelerate the convergence of PSO and avoid premature convergence is presented, which employs opposition-based learning for each particle and applies a dynamic Cauchy mutation on the best particle.