Fei Ming

1.5k total citations
36 papers, 938 citations indexed

About

Fei Ming is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Civil and Structural Engineering. According to data from OpenAlex, Fei Ming has authored 36 papers receiving a total of 938 indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Computational Theory and Mathematics, 31 papers in Artificial Intelligence and 6 papers in Civil and Structural Engineering. Recurrent topics in Fei Ming's work include Advanced Multi-Objective Optimization Algorithms (33 papers), Metaheuristic Optimization Algorithms Research (29 papers) and Evolutionary Algorithms and Applications (12 papers). Fei Ming is often cited by papers focused on Advanced Multi-Objective Optimization Algorithms (33 papers), Metaheuristic Optimization Algorithms Research (29 papers) and Evolutionary Algorithms and Applications (12 papers). Fei Ming collaborates with scholars based in China, New Zealand and Germany. Fei Ming's co-authors include Wenyin Gong, Ling Wang, Liang Gao, Ling Wang, Zuowen Liao, Chao Lu, Yaochu Jin, Shuijia Li, Dongcheng Li and Rui Li and has published in prestigious journals such as Expert Systems with Applications, Information Sciences and IEEE Transactions on Cybernetics.

In The Last Decade

Fei Ming

33 papers receiving 915 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Fei Ming China 17 639 612 166 132 70 36 938
Adriana Lara Mexico 11 660 1.0× 573 0.9× 62 0.4× 106 0.8× 112 1.6× 28 854
Ruwang Jiao China 14 486 0.8× 521 0.9× 52 0.3× 81 0.6× 55 0.8× 24 723
Saúl Zapotecas–Martínez Mexico 16 611 1.0× 551 0.9× 47 0.3× 83 0.6× 118 1.7× 52 827
Setsuko Sakai Japan 13 741 1.2× 821 1.3× 71 0.4× 86 0.7× 42 0.6× 44 1.0k
Yuren Zhou China 11 453 0.7× 515 0.8× 96 0.6× 61 0.5× 46 0.7× 18 694
Zefeng Chen China 11 558 0.9× 583 1.0× 64 0.4× 55 0.4× 156 2.2× 17 750
Cuie Yang China 14 406 0.6× 486 0.8× 80 0.5× 72 0.5× 51 0.7× 23 679
Gregorio Toscano‐Pulido Mexico 14 508 0.8× 428 0.7× 43 0.3× 57 0.4× 92 1.3× 41 718
Dan Guo China 5 540 0.8× 527 0.9× 47 0.3× 52 0.4× 131 1.9× 8 781

Countries citing papers authored by Fei Ming

Since Specialization
Citations

This map shows the geographic impact of Fei Ming'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 Fei Ming with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fei Ming more than expected).

Fields of papers citing papers by Fei Ming

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fei Ming. 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 Fei Ming. The network helps show where Fei Ming may publish in the future.

Co-authorship network of co-authors of Fei Ming

This figure shows the co-authorship network connecting the top 25 collaborators of Fei Ming. A scholar is included among the top collaborators of Fei Ming based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Fei Ming. Fei Ming is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Ming, Fei, Wenyin Gong, Bing Xue, Mengjie Zhang, & Yaochu Jin. (2025). Automated Configuration of Evolutionary Algorithms via Deep Reinforcement Learning for Constrained Multiobjective Optimization. IEEE Transactions on Cybernetics. 55(12). 5877–5890.
2.
Wu, Xinyi, et al.. (2025). Multimodal multi-objective optimization via multi-operator adaptation and clustering-based environmental selection. Swarm and Evolutionary Computation. 96. 101962–101962. 1 indexed citations
3.
Ming, Fei, Wenyin Gong, Bing Xue, Mengjie Zhang, & Yaochu Jin. (2025). An Evolutionary Framework for Multi-Objective Neural Architecture Search. IEEE Transactions on Evolutionary Computation. 1–1.
4.
Ming, Fei, et al.. (2024). Constrained multi-objective optimization evolutionary algorithm for real-world continuous mechanical design problems. Engineering Applications of Artificial Intelligence. 135. 108673–108673. 8 indexed citations
5.
Hu, Chengyu, et al.. (2024). Constrained multi-objective optimization with dual-swarm assisted competitive swarm optimizer. Swarm and Evolutionary Computation. 86. 101496–101496.
6.
Ming, Fei, et al.. (2024). Multi-stage multiform optimization for constrained multi-objective optimization. Neural Computing and Applications. 36(23). 14173–14235. 3 indexed citations
7.
Li, Rui, Ling Wang, Wenyin Gong, & Fei Ming. (2024). An Evolutionary Multitasking Memetic Algorithm for Multiobjective Distributed Heterogeneous Welding Flow Shop Scheduling. IEEE Transactions on Evolutionary Computation. 29(6). 2287–2298. 31 indexed citations
8.
Ming, Fei, et al.. (2024). Constrained Multiobjective Optimization via Relaxations on Both Constraints and Objectives. IEEE Transactions on Artificial Intelligence. 5(12). 6709–6722. 1 indexed citations
9.
Hu, Chengyu, et al.. (2024). A Diversity-Enhanced Tri-Stage Framework for Constrained Multiobjective Optimization. IEEE Transactions on Evolutionary Computation. 29(5). 2047–2061. 13 indexed citations
10.
Ming, Fei, et al.. (2024). Competitive Multitasking for Computational Resource Allocation in Evolutionary-Constrained Multiobjective Optimization. IEEE Transactions on Evolutionary Computation. 29(3). 809–821. 25 indexed citations
11.
Huang, Kuihua, et al.. (2023). Bi-directional search based on constraint relaxation for constrained multi-objective optimization problems with large infeasible regions. Expert Systems with Applications. 239. 122492–122492. 6 indexed citations
12.
Cheng, Xinyu, et al.. (2023). Multimodal multi-objective optimization via determinantal point process-assisted evolutionary algorithm. Neural Computing and Applications. 36(3). 1381–1411. 3 indexed citations
13.
Ming, Fei & Wenyin Gong. (2023). Exploring a Promising Region and Enhancing Decision Space Diversity for Multimodal Multi-Objective Optimization. Tsinghua Science & Technology. 29(2). 325–342. 3 indexed citations
14.
Ming, Fei, Wenyin Gong, Ling Wang, & Liang Gao. (2023). A Constraint-Handling Technique for Decomposition-Based Constrained Many-Objective Evolutionary Algorithms. IEEE Transactions on Systems Man and Cybernetics Systems. 53(12). 7783–7793. 26 indexed citations
15.
Ming, Fei, et al.. (2023). Multimodal multi-objective optimization with multi-stage-based evolutionary algorithm. Memetic Computing. 15(4). 377–389. 2 indexed citations
16.
Ming, Fei, et al.. (2022). An Ensemble Framework of Evolutionary Algorithm for Constrained Multi-Objective Optimization. Symmetry. 14(1). 116–116. 2 indexed citations
17.
Ming, Fei, Wenyin Gong, Ling Wang, & Liang Gao. (2022). Balancing Convergence and Diversity in Objective and Decision Spaces for Multimodal Multi-Objective Optimization. IEEE Transactions on Emerging Topics in Computational Intelligence. 7(2). 474–486. 51 indexed citations
18.
Ming, Fei, et al.. (2022). Constrained multi-objective optimization via two archives assisted push–pull evolutionary algorithm. Swarm and Evolutionary Computation. 75. 101178–101178. 14 indexed citations
19.
Ming, Fei, Wenyin Gong, & Ling Wang. (2022). A Two-Stage Evolutionary Algorithm With Balanced Convergence and Diversity for Many-Objective Optimization. IEEE Transactions on Systems Man and Cybernetics Systems. 52(10). 6222–6234. 85 indexed citations
20.
Gong, Wenyin, et al.. (2021). Two-Stage Data-Driven Evolutionary Optimization for High-Dimensional Expensive Problems. IEEE Transactions on Cybernetics. 53(4). 2368–2379. 48 indexed citations

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.

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