World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
35
Citations
4171
World Ranking
11820
National Ranking
361

Saber M. Elsayed 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 Saber M. Elsayed 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: 151 publications — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Saber M. Elsayed 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 Saber M. Elsayed 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: 35 D-Index — 20th percentile

20% of scientists in this discipline score the same or lower.

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

Overview

Saber M. Elsayed is affiliated with the University of New South Wales in Australia. Their research primarily spans the fields of Computer Science and Engineering, with a focus on several subfields including Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research, Industrial and Manufacturing Engineering, and Nuclear and High Energy Physics.

The scientist's recent papers include the following:

  • Quantum-Inspired Genetic Algorithm for Resource-Constrained Project-Scheduling (2021, IEEE Access)
  • Large-scale evolutionary optimization: A review and comparative study (2024, Swarm and Evolutionary Computation)
  • Solving electric vehicle-drone routing problem using memetic algorithm (2023, Swarm and Evolutionary Computation)
  • An evolutionary approach for resource constrained project scheduling with uncertain changes (2020, Computers & Operations Research)
  • Weighted pointwise prediction method for dynamic multiobjective optimization (2020, Information Sciences)

The scientist's research covers several main topics such as Advanced Multi-Objective Optimization Algorithms, Metaheuristic Optimization Algorithms Research, Resource-Constrained Project Scheduling, Evolutionary Algorithms and Applications, Scheduling and Optimization Algorithms, Particle Detector Development and Performance, and Robotic Path Planning Algorithms.

Frequent co-authors include:

  • Ruhul Sarker
  • Daryl Essam
  • Carlos A. Coello Coello
  • Kyle Robert Harrison
  • Ivan L. Garanovich

Elsayed has published extensively in several venues, with the most prominent being IEEE Access and Swarm and Evolutionary Computation. Other frequent publication venues include the Journal of Instrumentation, arXiv (Cornell University), and Applied Soft Computing.

In addition to journal articles, Saber M. Elsayed has authored a book titled Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling, published by Springer Nature in 2021.

Best Publications

  • Differential Evolution With Dynamic Parameters Selection for Optimization Problems

    Ruhul A. Sarker;Saber M. Elsayed;Tapabrata Ray

  • A new genetic algorithm for solving optimization problems

    Saber M. Elsayed;Ruhul A. Sarker;Daryl L. Essam

  • Improved Multi-operator Differential Evolution Algorithm for Solving Unconstrained Problems

    Karam M. Sallam;Saber M. Elsayed;Ripon K. Chakrabortty;Michael J. Ryan

  • Multi-operator based evolutionary algorithms for solving constrained optimization problems

    Saber M. Elsayed;Ruhul A. Sarker;Daryl L. Essam

  • An Improved Self-Adaptive Differential Evolution Algorithm for Optimization Problems

    S. M. Elsayed;R. A. Sarker;D. L. Essam

  • Evolutionary Algorithms for Dynamic Economic Dispatch Problems

    M. F. Zaman;Saber M. Elsayed;Tapabrata Ray;Ruhul A. Sarker

  • GA with a new multi-parent crossover for solving IEEE-CEC2011 competition problems

    Saber M. Elsayed;Ruhul A. Sarker;Daryl L. Essam

  • Differential evolution with multiple strategies for solving CEC2011 real-world numerical optimization problems

    Saber M. Elsayed;Ruhul A. Sarker;Daryl L. Essam

  • Consolidated optimization algorithm for resource-constrained project scheduling problems

    Saber Elsayed;Ruhul Sarker;Tapabrata Ray;Carlos Coello Coello

  • Self-adaptive mix of particle swarm methodologies for constrained optimization

    Saber M. Elsayed;Ruhul A. Sarker;Efrén Mezura-Montes

  • Landscape-based adaptive operator selection mechanism for differential evolution

    Karam M. Sallam;Saber M. Elsayed;Ruhul A. Sarker;Daryl Leslie Essam

  • Testing united multi-operator evolutionary algorithms on the CEC2014 real-parameter numerical optimization

    Saber M. Elsayed;Ruhul A. Sarker;Daryl Leslie Essam;Noha M. Hamza

  • Configuring two-algorithm-based evolutionary approach for solving dynamic economic dispatch problems

    Forhad Zaman;Saber M. Elsayed;Tapabrata Ray;Ruhul A. Sarker

  • A self-adaptive combined strategies algorithm for constrained optimization using differential evolution

    Saber M. Elsayed;Ruhul A. Sarker;Daryl L. Essam

  • Large-scale evolutionary optimization: A review and comparative study

    Unknown

  • A genetic algorithm for solving the CEC'2013 competition problems on real-parameter optimization

    Saber M. Elsayed;Ruhul A. Sarker;Daryl L. Essam

  • On an evolutionary approach for constrained optimization problem solving

    Saber M. Elsayed;Ruhul A. Sarker;Daryl L. Essam

  • Quantum-Inspired Genetic Algorithm for Resource-Constrained Project-Scheduling

    Hatem M. H. Saad;Ripon K. Chakrabortty;Saber Elsayed;Michael J. Ryan

  • Landscape-assisted multi-operator differential evolution for solving constrained optimization problems

    Karam M. Sallam;Saber M. Elsayed;Ruhul A. Sarker;Daryl Leslie Essam

  • Adaptive Sorting-Based Evolutionary Algorithm for Many-Objective Optimization

    Chao Liu;Qi Zhao;Bai Yan;Saber Elsayed

  • Testing united multi-operator evolutionary algorithms-II on single objective optimization problems

    Saber Elsayed;Noha Hamza;Ruhul Sarker

  • Differential evolution framework for big data optimization

    Saber M. Elsayed;Saber M. Elsayed;Ruhul A. Sarker

  • Neurodynamic differential evolution algorithm and solving CEC2015 competition problems

    Karam M. Sallam;Ruhul A. Sarker;Daryl L. Essam;Saber M. Elsayed

Frequent Co-Authors

Ruhul A. Sarker
Ruhul A. Sarker University of New South Wales
Daryl Essam
Daryl Essam University of New South Wales
Tapabrata Ray
Tapabrata Ray University of New South Wales
Karam M. Sallam
Karam M. Sallam University of Sharjah
Hussein A. Abbass
Hussein A. Abbass University of New South Wales
Michael J. Ryan
Michael J. Ryan University of New South Wales
Ripon Kumar Chakrabortty
Ripon Kumar Chakrabortty University of New South Wales
Qi Zhao
Qi Zhao University of Minnesota
Kalyanmoy Deb
Kalyanmoy Deb Michigan State University

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