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Aimin Zhou
Researcher at East China Normal University
Publications - 144
Citations - 6860
Aimin Zhou is an academic researcher from East China Normal University. The author has contributed to research in topics: Evolutionary algorithm & Evolutionary computation. The author has an hindex of 27, co-authored 121 publications receiving 5343 citations. Previous affiliations of Aimin Zhou include Beijing University of Posts and Telecommunications & University of Essex.
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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
Multiobjective optimization Test Instances for the CEC 2009 Special Session and Competition
Qingfu Zhang,Aimin Zhou,Shi-Zheng Zhao,Ponnuthurai Nagaratnam Suganthan,Wudong Liu,Santosh Tiwari +5 more
TL;DR: Multiobjective optimization Test Instances for the CEC 2009 Special Session and Competition Qingfu Zhang, Aimin Zhou, Shizheng Zhao, and Wudong Liu are authors of the report.
Journal ArticleDOI
RM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm
TL;DR: It is demonstrated that, compared with GDE3, RM-MEDA is not sensitive to algorithmic parameters, and has good scalability to the number of decision variables in the case of nonlinear variable linkages.
Journal ArticleDOI
A Population Prediction Strategy for Evolutionary Dynamic Multiobjective Optimization
TL;DR: This paper systematically compares PPS with a random initialization strategy and a hybrid initialization strategy on a variety of test instances with linear or nonlinear correlation between design variables to show that PPS is promising for dealing with dynamic environments.
Proceedings ArticleDOI
Combining Model-based and Genetics-based Offspring Generation for Multi-objective Optimization Using a Convergence Criterion
TL;DR: The proposed hybrid method is verified on widely used test problems and simulation results show that the method is effective in achieving Pareto-optimal solutions compared to two state-of-the-art evolutionary multi-objective algorithms.