Zhenkun Wang

2.9k citations
118 papers · 2.1k indexed · h-index 28

Zhenkun Wang

107 papers receiving 2.0k citations

Peers

Zhenkun Wang
Comparison fields: 5 of 119
  • Computational Theory and Mathematics 886
  • Artificial Intelligence 950
  • Industrial and Manufacturing Engineering 170
  • Management Science and Operations Research 192
  • Electronic, Optical and Magnetic Materials 257
Replace Tianjun Liao with:
Tianjun Liao China
M. Premkumar India
Shi Cheng China
Allan N. Zhang Singapore
Luca P. Carloni United States
Lihong Guo China
Pan Wang China
Conor Ryan Ireland
Bogdan Filipič Slovenia
Riqing Chen China
Zhenkun Wang relative to Tianjun Liao China Tianjun Liao's profile →
Citations per field
00.5×8.6×
Tianjun Liao · 1×
Citations per year

Countries citing papers authored by Zhenkun Wang

Since Specialization
Citations

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

Fields of papers citing papers by Zhenkun Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Zhenkun Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Zhenkun Wang Line = papers co-authored together Zhenkun Wang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20251
2 20245
3 20242
4 20241
5 20248
6 20244
7 20245
8 20244
9 202360
10 20231
11 202313
12 202327
13 20238
14 202154
15 202148
16 201872
17 201867
18 201883
19 201876
20 201511

About Zhenkun Wang

Zhenkun Wang is a scholar working on Computational Theory and Mathematics, Industrial and Manufacturing Engineering, Artificial Intelligence, Electronic, Optical and Magnetic Materials and Management Science and Operations Research, having authored 118 papers that have together received 2.1k indexed citations. Recurring topics across this work include Advanced Multi-Objective Optimization Algorithms (38 papers), Metaheuristic Optimization Algorithms Research (31 papers), Magnetic properties of thin films (15 papers), Evolutionary Algorithms and Applications (13 papers), Optimal Experimental Design Methods (10 papers), Magnetic Properties and Applications (9 papers), Vehicle Routing Optimization Methods (8 papers) and Metallic Glasses and Amorphous Alloys (7 papers). The work is most often cited by research in Computational Theory and Mathematics (886 citations), Artificial Intelligence (950 citations), Industrial and Manufacturing Engineering (170 citations), Management Science and Operations Research (192 citations) and Electronic, Optical and Magnetic Materials (257 citations). Zhenkun Wang has collaborated with scholars based in China, Hong Kong and Singapore. Frequent co-authors include Qingfu Zhang, Genghui Li, Maoguo Gong, Yew-Soon Ong, Licheng Jiao, Aimin Zhou, Hisao Ishibuchi, Jianping Luo, Qingfang Liu and Jianbo Wang. Their work appears in journals such as IEEE Transactions on Evolutionary Computation, Information Sciences, Swarm and Evolutionary Computation, IEEE Transactions on Cybernetics and IEEE Transactions on Emerging Topics in Computational Intelligence.

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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