Lishan Kang
- Artificial Intelligence top 5%
- Computational Theory and Mathematics top 5%
- Control and Systems Engineering top 10%
- Electrical and Electronic Engineering
- Industrial and Manufacturing Engineering top 10%
- Topics
- Metaheuristic Optimization Algorithms Research (45 papers)Evolutionary Algorithms and Applications (37 papers)Advanced Multi-Objective Optimization Algorithms (26 papers)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Lishan Kang
68 papers receiving 570 citations
Peers
Comparison fields: 5 of 88
- Artificial Intelligence 386
- Computational Theory and Mathematics 175
- Control and Systems Engineering 72
- Electrical and Electronic Engineering 65
- Industrial and Manufacturing Engineering 47
Countries citing papers authored by Lishan Kang
This map shows the geographic impact of Lishan Kang'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 Lishan Kang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lishan Kang more than expected).
Fields of papers citing papers by Lishan Kang
This network shows the impact of papers produced by Lishan Kang. 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 Lishan Kang. The network helps show where Lishan Kang may publish in the future.
Co-authorship network of co-authors of Lishan Kang
This figure shows the co-authorship network connecting the top 25 collaborators of Lishan Kang. A scholar is included among the top collaborators of Lishan Kang 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 Lishan Kang. Lishan Kang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 3 | |
| 3 | 13 | |
| 4 | 3 | |
| 5 | 10 | |
| 6 | 2 | |
| 7 | 5 | |
| 8 | 1 | |
| 9 | 4 | |
| 10 | An asynchronous parallel evolutionary algorithm (APEA) for solving complex non-linear real world optimization problems | 1 |
| 11 | 10 | |
| 12 | 64 | |
| 13 | 3 | |
| 14 | Evolutionary modeling of ordinary differential equations for dynamic systems | 3 |
| 15 | A hybrid evolutionary modeling algorithm for system of ordinary differential equations | 3 |
| 16 | 2 | |
| 17 | 4 | |
| 18 | Parallel multigrid algorithms based on wavelet multiresolution approximations. | 1 |
| 19 | Massively parallel algorithm I: A new class of lattice gas methods. | 1 |
| 20 | 0 |
About Lishan Kang
Lishan Kang is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Numerical Analysis, having authored 75 papers that have together received 610 indexed citations. Recurring topics across this work include Metaheuristic Optimization Algorithms Research (45 papers), Evolutionary Algorithms and Applications (37 papers) and Advanced Multi-Objective Optimization Algorithms (26 papers). The work is most often cited by research in Artificial Intelligence (386 citations), Computational Theory and Mathematics (175 citations) and Numerical Analysis (31 citations). Lishan Kang has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Hongqing Cao, Jingxian Yu, Jun He, Yuping Chen, Sanyou Zeng, Lixin Ding, Aimin Zhou, Xuesong Yan, Zhijian Wu and Xuejie Zhang. Their work appears in journals such as Cancer Research, IEEE Internet of Things Journal and Journal of Electroanalytical Chemistry.
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.