Tapabrata Ray

9.1k total citations · 1 hit paper
237 papers, 6.4k citations indexed

About

Tapabrata Ray is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Management Science and Operations Research. According to data from OpenAlex, Tapabrata Ray has authored 237 papers receiving a total of 6.4k indexed citations (citations by other indexed papers that have themselves been cited), including 131 papers in Computational Theory and Mathematics, 91 papers in Artificial Intelligence and 38 papers in Management Science and Operations Research. Recurrent topics in Tapabrata Ray's work include Advanced Multi-Objective Optimization Algorithms (126 papers), Metaheuristic Optimization Algorithms Research (82 papers) and Evolutionary Algorithms and Applications (43 papers). Tapabrata Ray is often cited by papers focused on Advanced Multi-Objective Optimization Algorithms (126 papers), Metaheuristic Optimization Algorithms Research (82 papers) and Evolutionary Algorithms and Applications (43 papers). Tapabrata Ray collaborates with scholars based in Australia, Singapore and United Kingdom. Tapabrata Ray's co-authors include Ruhul Sarker, Hemant Kumar Singh, K.M. Liew, Md Asafuddoula, Saber Elsayed, Kang Tai, Pankaj Saini, Warren Smith, Amitay Isaacs and Sreenatha G. Anavatti and has published in prestigious journals such as PLoS ONE, Journal of Cleaner Production and Scientific Reports.

In The Last Decade

Tapabrata Ray

227 papers receiving 6.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A Decomposition-Based Evolutionary Algorithm for Many Objective Optimization

2014 298 citations Md Asafuddoula, Tapabrata Ray et al. IEEE Transactions on Evolutionary Computation profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Tapabrata Ray Australia	40	3.3k	3.3k	905	803	672	237	6.4k
Himanshu Jain India	11	3.4k 1.0×	3.2k 1.0×	831 0.9×	634 0.8×	800 1.2×	23	6.8k
Bernhard Sendhoff Germany	34	4.5k 1.4×	4.3k 1.3×	1.1k 1.2×	677 0.8×	403 0.6×	165	7.7k
Aimin Zhou China	29	4.5k 1.4×	4.7k 1.4×	737 0.8×	617 0.8×	424 0.6×	176	6.9k
David A. Van Veldhuizen United States	9	3.0k 0.9×	2.7k 0.8×	552 0.6×	680 0.8×	555 0.8×	18	5.6k
Carlos M. Fonseca Portugal	29	5.3k 1.6×	4.4k 1.3×	1.1k 1.3×	1.4k 1.8×	964 1.4×	77	9.6k
Kaisa Miettinen Finland	40	4.8k 1.4×	3.1k 0.9×	1.9k 2.1×	1.8k 2.2×	640 1.0×	198	9.1k
N. Srinivas India	3	2.2k 0.7×	1.7k 0.5×	404 0.4×	822 1.0×	892 1.3×	14	5.5k
Ye Tian China	42	6.9k 2.1×	6.6k 2.0×	1.3k 1.5×	871 1.1×	566 0.8×	140	9.5k
Gary B. Lamont United States	23	5.0k 1.5×	4.8k 1.4×	1.0k 1.1×	1.4k 1.7×	1.1k 1.7×	129	10.9k

Countries citing papers authored by Tapabrata Ray

Since Specialization

Citations

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

Fields of papers citing papers by Tapabrata Ray

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tapabrata Ray

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

All Works

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20 of 20 papers shown

Limmer, Steffen, et al.. (2025). Design of fair and interpretable electric vehicle charging policies through genetic programming. Applied Energy. 404. 127176–127176.

Porter, Ben, et al.. (2024). A structural equation modeling approach to leveraging the power of extant sentiment analysis tools. Journal of Computational Social Science. 8(1).

Ray, Tapabrata, et al.. (2024). Using Bayesian Optimization to Improve Hyperparameter Search in TPOT. Proceedings of the Genetic and Evolutionary Computation Conference. 340–348. 1 indexed citations

Singh, Hemant Kumar, et al.. (2024). Towards solving expensive optimization problems with heterogeneous constraint costs. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 2032–2040.

Singh, Hemant Kumar, et al.. (2023). A benchmark test suite for evolutionary multi-objective multi-concept optimization. Swarm and Evolutionary Computation. 84. 101429–101429. 7 indexed citations

Singh, Hemant Kumar, et al.. (2023). Vertical-Axis Wind Turbine Design Using Surrogate-assisted Optimization with Physical Experiments In-loop. Proceedings of the Genetic and Evolutionary Computation Conference. 1391–1399. 1 indexed citations

Ray, Tapabrata, et al.. (2022). An Iterative Two-Stage Multifidelity Optimization Algorithm for Computationally Expensive Problems. IEEE Transactions on Evolutionary Computation. 27(3). 520–534. 9 indexed citations

Singh, Hemant Kumar, et al.. (2022). A Steady-State Algorithm for Solving Expensive Multiobjective Optimization Problems With Nonparallelizable Evaluations. IEEE Transactions on Evolutionary Computation. 27(5). 1544–1558. 10 indexed citations

Singh, Hemant Kumar, Tapabrata Ray, Md Juel Rana, et al.. (2022). Investigating the Use of Linear Programming and Evolutionary Algorithms for Multi-Objective Electric Vehicle Charging Problem. IEEE Access. 10. 115322–115337. 6 indexed citations

10.

Ray, Tapabrata, et al.. (2022). Towards identification of solutions of interest for multi-objective problems considering both objective and variable space information. Applied Soft Computing. 119. 108505–108505. 7 indexed citations

11.

Singh, Hemant Kumar, et al.. (2021). Partial Evaluation Strategies for Expensive Evolutionary Constrained Optimization. IEEE Transactions on Evolutionary Computation. 25(6). 1103–1117. 33 indexed citations

12.

Singh, Hemant Kumar, et al.. (2021). A Multifidelity Approach for Bilevel Optimization With Limited Computing Budget. IEEE Transactions on Evolutionary Computation. 26(2). 392–399. 6 indexed citations

13.

Gharleghi, Ramtin, Nigel Jepson, Zhen Luo, et al.. (2021). A multi-objective optimization of stent geometries. Journal of Biomechanics. 125. 110575–110575. 19 indexed citations

14.

Singh, Hemant Kumar, et al.. (2019). A multiple surrogate assisted multi/many-objective multi-fidelity evolutionary algorithm. Information Sciences. 502. 537–557. 21 indexed citations

15.

Singh, Hemant Kumar, et al.. (2019). A Multiple Surrogate Assisted Decomposition-Based Evolutionary Algorithm for Expensive Multi/Many-Objective Optimization. IEEE Transactions on Evolutionary Computation. 23(6). 1000–1014. 113 indexed citations

16.

Chand, Shelvin, et al.. (2018). Genetic Programming With Mixed-Integer Linear Programming-Based Library Search. IEEE Transactions on Evolutionary Computation. 22(5). 733–747. 22 indexed citations

17.

Singh, Hemant Kumar, et al.. (2018). Distance-Based Subset Selection for Benchmarking in Evolutionary Multi/Many-Objective Optimization. IEEE Transactions on Evolutionary Computation. 23(5). 904–912. 59 indexed citations

18.

Asafuddoula, Md, Hemant Kumar Singh, & Tapabrata Ray. (2017). An Enhanced Decomposition-Based Evolutionary Algorithm With Adaptive Reference Vectors. IEEE Transactions on Cybernetics. 48(8). 2321–2334. 91 indexed citations

19.

Singh, Hemant Kumar, et al.. (2017). Bridging the Gap: Many-Objective Optimization and Informed Decision-Making. IEEE Transactions on Evolutionary Computation. 21(5). 813–820. 46 indexed citations

20.

Singh, Hemant Kumar, et al.. (2016). An approach to generate comprehensive piecewise linear interpolation of pareto outcomes to aid decision making. Journal of Global Optimization. 68(1). 71–93. 3 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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