Q
Qingfu Zhang
Researcher at City University of Hong Kong
Publications - 386
Citations - 29736
Qingfu Zhang is an academic researcher from City University of Hong Kong. The author has contributed to research in topics: Evolutionary algorithm & Multi-objective optimization. The author has an hindex of 62, co-authored 346 publications receiving 23258 citations. Previous affiliations of Qingfu Zhang include Center for Information Technology & The Chinese University of Hong Kong.
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.
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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.
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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
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Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters
TL;DR: A novel method, called composite DE (CoDE), has been proposed, which uses three trial vector generation strategies and three control parameter settings and randomly combines them to generate trial vectors.
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An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition
TL;DR: A unified paradigm, which combines dominance- and decomposition-based approaches, for many-objective optimization, is suggested, which shows highly competitive performance on all the constrained optimization problems.