Milan Tuba
Impact in
- Artificial Intelligence top 0.5%
- Metaheuristic Optimization Algorithms Research
- Evolutionary Algorithms and Applications
Papers in
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- Metaheuristic Optimization Algorithms Research 56
- Evolutionary Algorithms and Applications 19
- Target Tracking and Data Fusion in Sensor Networks 9
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- Robotic Path Planning Algorithms 9
- Co-authors
- Nebojša Bačanin (59 shared papers)Eva Tuba (60 shared papers)Raka Jovanović (31 shared papers)Ivana Strumberger (34 shared papers)Adis Alihodžić (13 shared papers)Marko Beko (25 shared papers)Timea Bezdan (8 shared papers)Ivona Brajević (5 shared papers)
In The Last Decade
Milan Tuba
154 papers receiving 3.5k citations
Peers
Comparison fields: 5 of 143
- Artificial Intelligence 1.8k
- Computer Vision and Pattern Recognition 850
- Computer Networks and Communications 874
- Industrial and Manufacturing Engineering 338
- Computational Theory and Mathematics 502
Countries citing papers authored by Milan Tuba
This map shows the geographic impact of Milan Tuba'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 Milan Tuba with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Milan Tuba more than expected).
Fields of papers citing papers by Milan Tuba
This network shows the impact of papers produced by Milan Tuba. 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 Milan Tuba. The network helps show where Milan Tuba may publish in the future.
Co-authors
The 25 scholars most cited alongside Milan Tuba, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 160 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2012 | 115 | |
| 2 | 2020 | 102 | |
| 3 | 2014 | 96 | |
| 4 | 2019 | 94 | |
| 5 | 2014 | 92 | |
| 6 | 2011 | 90 | |
| 7 | 2012 | 89 | |
| 8 | 2019 | 82 | |
| 9 | 2014 | 82 | |
| 10 | 2012 | 81 | |
| 11 | 2019 | 78 | |
| 12 | 2014 | 72 | |
| 13 | Modified cuckoo search algorithm for unconstrained optimization problems | 2011 | 72 |
| 14 | 2020 | 68 | |
| 15 | 2020 | 66 | |
| 16 | 2021 | 65 | |
| 17 | 2019 | 65 | |
| 18 | 2019 | 60 | |
| 19 | 2019 | 59 | |
| 20 | 2018 | 57 |
About Milan Tuba
Milan Tuba is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications, Electrical and Electronic Engineering and Computational Theory and Mathematics, having authored 160 papers that have together received 3.7k indexed citations. Recurring topics across this work include Metaheuristic Optimization Algorithms Research (56 papers), Advanced Multi-Objective Optimization Algorithms (22 papers), Evolutionary Algorithms and Applications (19 papers), Indoor and Outdoor Localization Technologies (19 papers), Vehicle Routing Optimization Methods (15 papers), Energy Efficient Wireless Sensor Networks (11 papers), Target Tracking and Data Fusion in Sensor Networks (9 papers) and Robotic Path Planning Algorithms (9 papers). The work is most often cited by research in Artificial Intelligence (1.8k citations), Computer Vision and Pattern Recognition (850 citations), Computer Networks and Communications (874 citations), Industrial and Manufacturing Engineering (338 citations) and Computational Theory and Mathematics (502 citations). Milan Tuba has collaborated with scholars based in Serbia, Portugal and Romania. Frequent co-authors include Nebojša Bačanin, Eva Tuba, Raka Jovanović, Ivana Strumberger, Adis Alihodžić, Marko Beko, Timea Bezdan, Ivona Brajević, Miodrag Živković and Dana Simian. Their work appears in journals such as Studies in Informatics and Control, Applied Soft Computing, PLANT PHYSIOLOGY, IEEE Wireless Communications Letters and IEEE Access.
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.