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Ye Tian
Researcher at Anhui University
Publications - 77
Citations - 6547
Ye Tian is an academic researcher from Anhui University. The author has contributed to research in topics: Evolutionary algorithm & Multi-objective optimization. The author has an hindex of 23, co-authored 56 publications receiving 3336 citations. Previous affiliations of Ye Tian include University of Surrey & Hunan University.
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PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum]
TL;DR: PlatEMO as discussed by the authors is a MATLAB platform for evolutionary multi-objective optimization, which includes more than 50 multiobjective evolutionary algorithms and more than 100 multobjective test problems, along with several widely used performance indicators.
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PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization
TL;DR: The main features of PlatEMO are introduced and how to use it for performing comparative experiments, embedding new algorithms, creating new test problems, and developing performance indicators are illustrated.
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A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization
Xingyi Zhang,Ye Tian,Yaochu Jin +2 more
TL;DR: A knee point-driven EA to solve MaOPs by showing that knee points are naturally most preferred among nondominated solutions if no explicit user preferences are given and enhancing the convergence performance in many-objective optimization.
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An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility
TL;DR: The proposed MOEA based on an enhanced inverted generational distance indicator is versatile for solving problems with various types of Pareto fronts, outperforming several state-of-the-art evolutionary algorithms for multiobjective and many-objective optimization.
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An Efficient Approach to Nondominated Sorting for Evolutionary Multiobjective Optimization
TL;DR: In this paper, a novel, computationally efficient approach to nondominated sorting is proposed, termed efficient nondominated sort (ENS), where a solution to be assigned to a front needs to be compared only with those that have already been assigned toA front, thereby avoiding many unnecessary dominance comparisons.