Ran Cheng
Impact in
- Computational Theory and Mathematics top 0.01%
- Advanced Multi-Objective Optimization Algorithms
- Artificial Intelligence top 0.2%
- Metaheuristic Optimization Algorithms Research
- Evolutionary Algorithms and Applications
Papers in
-
- Advanced Multi-Objective Optimization Algorithms 72
-
- Metaheuristic Optimization Algorithms Research 32
- Evolutionary Algorithms and Applications 14
- Reinforcement Learning in Robotics 9
- Machine Learning and Data Classification 6
- Co-authors
- Yaochu Jin (64 shared papers)Xingyi Zhang (24 shared papers)Ye Tian (21 shared papers)Bernhard Sendhoff (4 shared papers)Markus Olhofer (4 shared papers)Cheng He (36 shared papers)Xin Yao (15 shared papers)Kay Chen Tan (14 shared papers)
- Journals
- IEEE Transactions on Evolutionary Computation (24 papers)Information Sciences (8 papers)IEEE Transactions on Cybernetics (8 papers)Complex & Intelligent Systems (8 papers)IEEE Computational Intelligence Magazine (6 papers)
- Partner nations
- ChinaUnited KingdomHong Kong
In The Last Decade
Ran Cheng
199 papers receiving 14.8k citations
Ran Cheng's Hit Papers
Peers
Comparison fields: 5 of 148
- Computational Theory and Mathematics 8.4k
- Artificial Intelligence 3.4k
- Industrial and Manufacturing Engineering 488
- Computer Vision and Pattern Recognition 1k
- Control and Systems Engineering 359
Countries citing papers authored by Ran Cheng
This map shows the geographic impact of Ran Cheng's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ran Cheng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ran Cheng more than expected).
Fields of papers citing papers by Ran Cheng
This network shows the impact of papers produced by Ran Cheng. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ran Cheng. The network helps show where Ran Cheng may publish in the future.
Co-authors
The 25 scholars most cited alongside Ran Cheng, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 218 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum] Hit paper breakdown → | 2017 | 2106 |
| 2 | A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization Hit paper breakdown → | 2016 | 1404 |
| 3 | A Competitive Swarm Optimizer for Large Scale Optimization Hit paper breakdown → | 2014 | 887 |
| 4 | A social learning particle swarm optimization algorithm for scalable optimization Hit paper breakdown → | 2014 | 645 |
| 5 | An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility Hit paper breakdown → | 2017 | 593 |
| 6 | A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization Hit paper breakdown → | 2016 | 538 |
| 7 | An Efficient Approach to Nondominated Sorting for Evolutionary Multiobjective Optimization Hit paper breakdown → | 2014 | 449 |
| 8 | A benchmark test suite for evolutionary many-objective optimization Hit paper breakdown → | 2017 | 411 |
| 9 | A competitive mechanism based multi-objective particle swarm optimizer with fast convergence Hit paper breakdown → | 2017 | 350 |
| 10 | Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems Hit paper breakdown → | 2017 | 348 |
| 11 | Test Problems for Large-Scale Multiobjective and Many-Objective Optimization Hit paper breakdown → | 2016 | 338 |
| 12 | A Multiobjective Evolutionary Algorithm Using Gaussian Process-Based Inverse Modeling Hit paper breakdown → | 2015 | 323 |
| 13 | A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization Hit paper breakdown → | 2018 | 322 |
| 14 | 2016 | 297 | |
| 15 | Evolutionary Large-Scale Multi-Objective Optimization: A Survey Hit paper breakdown → | 2021 | 296 |
| 16 | Accelerating Large-Scale Multiobjective Optimization via Problem Reformulation Hit paper breakdown → | 2019 | 280 |
| 17 | Mapping global lake dynamics reveals the emerging roles of small lakes Hit paper breakdown → | 2022 | 242 |
| 18 | 2020 | 226 | |
| 19 | 2021 | 215 | |
| 20 | 2021 | 193 |
About Ran Cheng
Ran Cheng is a scholar working on Computational Theory and Mathematics, Artificial Intelligence, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Control and Systems Engineering, having authored 218 papers that have together received 15k indexed citations. Recurring topics across this work include Advanced Multi-Objective Optimization Algorithms (72 papers), Metaheuristic Optimization Algorithms Research (32 papers), Advanced Neural Network Applications (20 papers), Evolutionary Algorithms and Applications (14 papers), Reinforcement Learning in Robotics (9 papers), Machine Learning and Data Classification (6 papers), Microgrid Control and Optimization (5 papers) and Power Quality and Harmonics (4 papers). The work is most often cited by research in Computational Theory and Mathematics (8.4k citations), Artificial Intelligence (3.4k citations), Industrial and Manufacturing Engineering (488 citations), Computer Vision and Pattern Recognition (1k citations) and Control and Systems Engineering (359 citations). Ran Cheng has collaborated with scholars based in China, United Kingdom and Hong Kong. Frequent co-authors include Yaochu Jin, Xingyi Zhang, Ye Tian, Bernhard Sendhoff, Markus Olhofer, Cheng He, Xin Yao, Kay Chen Tan, Miqing Li and Danial Yazdani. Their work appears in journals such as IEEE Transactions on Evolutionary Computation, Information Sciences, IEEE Transactions on Cybernetics, Complex & Intelligent Systems and IEEE Computational Intelligence Magazine.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.