World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
40
Citations
10375
World Ranking
9104
National Ranking
358

Shahryar Rahnamayan publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Shahryar Rahnamayan sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 210 publications — 50th percentile

50% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Shahryar Rahnamayan D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Shahryar Rahnamayan sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 40 D-Index — 37th percentile

37% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Shahryar Rahnamayan is affiliated with the University of Ontario Institute of Technology in Canada, focusing primarily on research within the field of Computer Science. Their work spans various subfields, particularly in Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Computational Theory and Mathematics, and Management Science and Operations Research.

Their research topics cover a range of areas including AI in cancer detection, metaheuristic optimization algorithms research, advanced multi-objective optimization algorithms, radiomics and machine learning in medical imaging, digital imaging for blood diseases, machine learning and data classification, and evolutionary algorithms and applications.

Among recent papers authored or co-authored by Rahnamayan are:

  • "Analysis, modeling, and multi-objective optimization of machining Inconel 718 with nano-additives based minimum quantity coolant" (2021, Applied Soft Computing)
  • "Biased data, biased AI: deep networks predict the acquisition site of TCGA images" (2023, Diagnostic Pathology)
  • "Reference-point-based multi-objective optimization algorithm with opposition-based voting scheme for multi-label feature selection" (2020, Information Sciences)
  • "Semisupervised Hyperspectral Image Classification Using a Probabilistic Pseudo-Label Generation Framework" (2022, IEEE Transactions on Geoscience and Remote Sensing)
  • "Machine learning-based framework to cover optimal Pareto-front in many-objective optimization" (2022, Complex & Intelligent Systems)

Frequent co-authors collaborating with Rahnamayan include Azam Asilian Bidgoli, Hamid R. Tizhoosh, Taher Dehkharghanian, and Kalyanmoy Deb.

The scientist has published extensively in certain venues, with a notable presence in arXiv (Cornell University), SSRN Electronic Journal, Scientific Reports, Research Square, and Applied Soft Computing.

Rahnamayan has also contributed to book literature, with a publication through IntechOpen titled Swarm Intelligence - Recent Advances and Current Applications released in 2022.

Best Publications

  • Opposition-Based Differential Evolution

    S. Rahnamayan;H.R. Tizhoosh;M.M.A. Salama

  • Enhancing particle swarm optimization using generalized opposition-based learning

    Hui Wang;Zhijian Wu;Shahryar Rahnamayan;Yong Liu

  • Metaheuristics in large-scale global continues optimization

    Sedigheh Mahdavi;Mohammad Ebrahim Shiri;Shahryar Rahnamayan

  • Diversity enhanced particle swarm optimization with neighborhood search

    Hui Wang;Hui Sun;Changhe Li;Shahryar Rahnamayan

  • Quasi-oppositional Differential Evolution

    S. Rahnamayan;H.R. Tizhoosh;M.M.A. Salama

  • A novel population initialization method for accelerating evolutionary algorithms

    Shahryar Rahnamayan;Hamid R. Tizhoosh;Magdy M. A. Salama

  • Opposition based learning: A literature review

    Sedigheh Mahdavi;Shahryar Rahnamayan;Kalyanmoy Deb

  • Opposition versus randomness in soft computing techniques

    Shahryar Rahnamayan;Hamid R. Tizhoosh;Magdy M. A. Salama

  • Gaussian Bare-Bones Differential Evolution

    Hui Wang;S. Rahnamayan;Hui Sun;M. G. H. Omran

  • Multi-strategy ensemble artificial bee colony algorithm

    Hui Wang;Zhijian Wu;Shahryar Rahnamayan;Hui Sun

  • Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems

    Hui Wang;Zhijian Wu;Shahryar Rahnamayan

  • Opposition-Based Differential Evolution Algorithms

    S. Rahnamayan;H.R. Tizhoosh;M.M.A. Salama

  • Firefly algorithm with random attraction

    Hui Wang;Wenjun Wang;Hui Sun;Shahryar Rahnamayan

  • Opposition-Based Differential Evolution for Optimization of Noisy Problems

    S. Rahnamayan;H.R. Tizhoosh;M.M.A. Salama

  • Parallel differential evolution with self-adapting control parameters and generalized opposition-based learning for solving high-dimensional optimization problems

    Hui Wang;Shahryar Rahnamayan;Zhijian Wu

  • Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism

    Hui Wang;Zhihua Cui;Hui Sun;Shahryar Rahnamayan

  • Opposition-Based Differential Evolution (ODE) with Variable Jumping Rate

    S. Rahnamayan;H.R. Tizhoosh;M.M.A. Salama

  • Solving large scale optimization problems by opposition-based differential evolution (ODE)

    Shahryar Rahnamayan;G. Gary Wang

  • A new cuckoo search algorithm with hybrid strategies for flow shop scheduling problems

    Hui Wang;Wenjun Wang;Hui Sun;Zhihua Cui

  • Center-based sampling for population-based algorithms

    Shahryar Rahnamayan;G. Gary Wang

  • Image thresholding using micro opposition-based Differential Evolution (Micro-ODE)

    S. Rahnamayan;H.R. Tizhoosh

Frequent Co-Authors

Hui Wang
Hui Wang University of Ulster
Kalyanmoy Deb
Kalyanmoy Deb Michigan State University
Magdy M. A. Salama
Magdy M. A. Salama University of Waterloo
Greg F. Naterer
Greg F. Naterer University of Prince Edward Island
G. Gary Wang
G. Gary Wang University of Science and Technology of China
Jeng-Shyang Pan
Jeng-Shyang Pan Shandong University of Science and Technology
Zhihua Cui
Zhihua Cui Taiyuan University of Science and Technology
Ibrahim Dincer
Ibrahim Dincer University of Ontario Institute of Technology
Bekir Sami Yilbas
Bekir Sami Yilbas King Fahd University of Petroleum and Minerals

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