Sunith Bandaru

1.2k total citations
46 papers, 762 citations indexed

About

Sunith Bandaru is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Industrial and Manufacturing Engineering. According to data from OpenAlex, Sunith Bandaru has authored 46 papers receiving a total of 762 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Computational Theory and Mathematics, 19 papers in Artificial Intelligence and 14 papers in Industrial and Manufacturing Engineering. Recurrent topics in Sunith Bandaru's work include Advanced Multi-Objective Optimization Algorithms (26 papers), Metaheuristic Optimization Algorithms Research (16 papers) and Evolutionary Algorithms and Applications (13 papers). Sunith Bandaru is often cited by papers focused on Advanced Multi-Objective Optimization Algorithms (26 papers), Metaheuristic Optimization Algorithms Research (16 papers) and Evolutionary Algorithms and Applications (13 papers). Sunith Bandaru collaborates with scholars based in Sweden, India and United States. Sunith Bandaru's co-authors include Kalyanmoy Deb, Amos H.C. Ng, Julian Blank, Haitham Seada, Yashesh Dhebar, Cem C. Tutum, António Gaspar‐Cunha, David Greiner, Anna Syberfeldt and Martin Andersson and has published in prestigious journals such as European Journal of Operational Research, Expert Systems with Applications and IEEE Access.

In The Last Decade

Sunith Bandaru

43 papers receiving 731 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Sunith Bandaru Sweden 16 360 335 181 70 70 46 762
Charles Newton Australia 10 333 0.9× 398 1.2× 147 0.8× 31 0.4× 52 0.7× 13 796
Tatsuya Okabe Germany 8 340 0.9× 349 1.0× 72 0.4× 55 0.8× 159 2.3× 12 757
Myriam Delgado Brazil 19 244 0.7× 503 1.5× 154 0.9× 88 1.3× 92 1.3× 87 899
Leonora Bianchi Switzerland 8 149 0.4× 268 0.8× 320 1.8× 86 1.2× 24 0.3× 12 748
Raj Subbu United States 13 159 0.4× 274 0.8× 69 0.4× 110 1.6× 43 0.6× 36 587
Reza Moghdani Iran 14 147 0.4× 295 0.9× 252 1.4× 60 0.9× 17 0.2× 19 729
Yu Setoguchi Japan 8 703 2.0× 613 1.8× 65 0.4× 183 2.6× 45 0.6× 9 923
Praveen Kumar Tripathi India 7 176 0.5× 258 0.8× 56 0.3× 49 0.7× 38 0.5× 27 582
Christian von Lücken Paraguay 7 296 0.8× 252 0.8× 67 0.4× 88 1.3× 36 0.5× 18 698

Countries citing papers authored by Sunith Bandaru

Since Specialization
Citations

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

Fields of papers citing papers by Sunith Bandaru

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sunith Bandaru

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

All Works

20 of 20 papers shown
1.
Bandaru, Sunith, et al.. (2025). Imbalanced data oversampling through subspace optimization with Bayesian reinforcement. Artificial Intelligence Review. 59(1).
2.
Bandaru, Sunith, et al.. (2025). Comparison of unsupervised image anomaly detection models for sheet metal glue lines. Engineering Applications of Artificial Intelligence. 153. 110740–110740.
3.
Bandaru, Sunith, et al.. (2025). Fashion image generation using generative adversarial neural network. World Journal of Advanced Research and Reviews. 25(1). 850–853. 1 indexed citations
4.
Bandaru, Sunith, et al.. (2024). Enhanced Decision Support for Multi-Objective Factory Layout Optimization: Integrating Human Well-Being and System Performance Analysis. Applied Sciences. 14(22). 10736–10736. 1 indexed citations
5.
6.
Bandaru, Sunith, et al.. (2024). Mimer: A Web-Based Tool for Knowledge Discovery in Multi-Criteria Decision Support [Application Notes]. IEEE Computational Intelligence Magazine. 19(3). 73–87. 2 indexed citations
7.
Chen, Siyuan, et al.. (2024). Enhancing Digital Twins With Deep Reinforcement Learning: A Use Case in Maintenance Prioritization. Lund University Publications (Lund University). 1611–1622. 1 indexed citations
8.
Bandaru, Sunith, et al.. (2023). A Performance Indicator for Interactive Evolutionary Multiobjective Optimization Methods. IEEE Transactions on Evolutionary Computation. 28(3). 778–787. 6 indexed citations
9.
Bandaru, Sunith, et al.. (2022). Enabling Knowledge Discovery from Simulation-Based Multi-Objective Optimization in Reconfigurable Manufacturing Systems. 2022 Winter Simulation Conference (WSC). 1794–1805. 2 indexed citations
10.
Ng, Amos H.C., et al.. (2022). A digital twin based framework for detection, diagnosis, and improvement of throughput bottlenecks. Journal of Manufacturing Systems. 66. 92–106. 63 indexed citations
11.
Blank, Julian, Kalyanmoy Deb, Yashesh Dhebar, Sunith Bandaru, & Haitham Seada. (2020). Generating Well-Spaced Points on a Unit Simplex for Evolutionary Many-Objective Optimization. IEEE Transactions on Evolutionary Computation. 25(1). 48–60. 54 indexed citations
12.
Bandaru, Sunith, et al.. (2019). A parameterless performance metric for reference-point based multi-objective evolutionary algorithms. Proceedings of the Genetic and Evolutionary Computation Conference. 499–506. 12 indexed citations
13.
Bandaru, Sunith, et al.. (2018). A framework for simulation-based multi-objective optimization and knowledge discovery of machining process. The International Journal of Advanced Manufacturing Technology. 98(9-12). 2469–2486. 15 indexed citations
14.
Andersson, Martin, Sunith Bandaru, & Amos H.C. Ng. (2016). Towards optimal algorithmic parameters for simulation-based multi-objective optimization. 3 indexed citations
15.
Ng, Amos H.C., et al.. (2016). Innovative Design and Analysis of Production Systems by Multi-objective Optimization and Data Mining. Procedia CIRP. 50. 665–671. 7 indexed citations
16.
Ng, Amos H.C., et al.. (2015). An interactive decision support system using simulation-based optimization and data mining. Winter Simulation Conference. 2112–2123. 2 indexed citations
17.
Ng, Amos H.C., et al.. (2015). An interactive decision support system using simulation-based optimization and data mining. 2015 Winter Simulation Conference (WSC). 2112–2123. 7 indexed citations
18.
Deb, Kalyanmoy, et al.. (2014). Non-uniform mapping in real-coded genetic algorithms. 2237–2244. 19 indexed citations
19.
Bandaru, Sunith, Amos H.C. Ng, & Kalyanmoy Deb. (2014). On the performance of classification algorithms for learning Pareto-dominance relations. University Library of Skövde (University of Skövde). 1139–1146. 32 indexed citations
20.
Deb, Kalyanmoy, Sunith Bandaru, David Greiner, António Gaspar‐Cunha, & Cem C. Tutum. (2013). An integrated approach to automated innovization for discovering useful design principles: Case studies from engineering. Applied Soft Computing. 15. 42–56. 51 indexed citations

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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