Zhenshou Song

12 papers receiving 432 citations

Hit Papers

A Kriging-Assisted Two-Archive Evolutionary Algorithm for...2021202620222024202150100150

Peers

Zhenshou Song
Comparison fields: 5 of 59
  • Artificial Intelligence 326
  • Computational Theory and Mathematics 264
  • Management Science and Operations Research 66
  • Electrical and Electronic Engineering 41
  • Renewable Energy, Sustainability and the Environment 40
Replace Yuanchao Liu with:
Yuanchao Liu China
Yaru Hu China
Xinfang Ji China
Linjun He China
Xuanxuan Ban China
Lucas Bradstreet Australia
Minglei Yin China
Kaname Narukawa Japan
Chunteng Bao China
Zhenshou Song relative to Yuanchao Liu China Yuanchao Liu's profile →
Citations per field
00.5×10×20×26.5×
Yuanchao Liu · 1×
Citations per year

Countries citing papers authored by Zhenshou Song

Since Specialization
Citations

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

Fields of papers citing papers by Zhenshou Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zhenshou Song

This figure shows the co-authorship network connecting the top 25 collaborators of Zhenshou Song. A scholar is included among the top collaborators of Zhenshou Song 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 Zhenshou Song. Zhenshou Song is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

12 of 12 papers shown
#WorkIndexed citations
1 11
2 37
3 18
4
A Kriging-Assisted Two-Archive Evolutionary Algorithm for Expensive Many-Objective Optimizationbreakdown →
172
5 32
6 22
7 1
8 38
9 11
10 44
11 27
12 24

About Zhenshou Song

Zhenshou Song is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Management Science and Operations Research, having authored 12 papers that have together received 437 indexed citations. Recurring topics across this work include Metaheuristic Optimization Algorithms Research (11 papers), Advanced Multi-Objective Optimization Algorithms (7 papers) and Evolutionary Algorithms and Applications (5 papers). The work is most often cited by research in Computational Theory and Mathematics (264 citations), Artificial Intelligence (326 citations) and Management Science and Operations Research (66 citations). Zhenshou Song has collaborated with scholars based in China, Canada and United Kingdom. Frequent co-authors include Handing Wang, Yaochu Jin, Cheng He, Chunquan Li, Peter Liu, Jinghui Fan, Bing Xue, Mengjie Zhang and Zeyuan Yan. Their work appears in journals such as IEEE Access, IEEE Transactions on Evolutionary Computation and IEEE Transactions on Systems Man and Cybernetics Systems.

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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