Kanchan Rajwar
- Artificial Intelligence top 10%
- Computational Theory and Mathematics top 10%
- Electrical and Electronic Engineering
- Industrial and Manufacturing Engineering top 10%
- Control and Systems Engineering
- Co-authors
- Kusum DeepSwagatam Das
- Topics
- Metaheuristic Optimization Algorithms Research (6 papers)Advanced Multi-Objective Optimization Algorithms (6 papers)Evolutionary Algorithms and Applications (4 papers)
- Cited by
- Artificial IntelligenceComputational Theory and MathematicsIndustrial and Manufacturing Engineering
- Journals
- Expert Systems with ApplicationsArtificial Intelligence ReviewAlexandria Engineering Journal
- Partner nations
- India
In The Last Decade
Kanchan Rajwar
6 papers receiving 302 citations
Hit Papers
Peers
Comparison fields: 5 of 73
- Artificial Intelligence 153
- Computational Theory and Mathematics 70
- Electrical and Electronic Engineering 48
- Industrial and Manufacturing Engineering 42
- Control and Systems Engineering 36
Countries citing papers authored by Kanchan Rajwar
This map shows the geographic impact of Kanchan Rajwar'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 Kanchan Rajwar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kanchan Rajwar more than expected).
Fields of papers citing papers by Kanchan Rajwar
This network shows the impact of papers produced by Kanchan Rajwar. 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 Kanchan Rajwar. The network helps show where Kanchan Rajwar may publish in the future.
Co-authorship network of co-authors of Kanchan Rajwar
This figure shows the co-authorship network connecting the top 25 collaborators of Kanchan Rajwar. A scholar is included among the top collaborators of Kanchan Rajwar 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 Kanchan Rajwar. Kanchan Rajwar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 8 | |
| 5 | 12 | |
| 6 | An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challengesbreakdown → | 287 |
About Kanchan Rajwar
Kanchan Rajwar is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Industrial and Manufacturing Engineering, having authored 6 papers that have together received 312 indexed citations. Recurring topics across this work include Metaheuristic Optimization Algorithms Research (6 papers), Advanced Multi-Objective Optimization Algorithms (6 papers) and Evolutionary Algorithms and Applications (4 papers). The work is most often cited by research in Artificial Intelligence (153 citations), Computational Theory and Mathematics (70 citations) and Industrial and Manufacturing Engineering (42 citations). Kanchan Rajwar has collaborated with scholars based in India. Frequent co-authors include Kusum Deep and Swagatam Das. Their work appears in journals such as Expert Systems with Applications, Artificial Intelligence Review and Alexandria Engineering Journal.
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.