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D-Index
64
Citations
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World Ranking
129
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43

Computer Science

D-Index
65
Citations
15506
World Ranking
2474
National Ranking
333

Gai-Ge Wang 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 Gai-Ge Wang 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: 144 publications — 24th percentile

24% of scientists in this discipline score the same or lower.

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

Gai-Ge Wang 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 Gai-Ge Wang 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: 65 D-Index — 83rd percentile

83% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Gai-Ge Wang is affiliated with the Ocean University of China in China and has contributed extensively to the fields of computer science and engineering. Their research spans several specialized areas, with a primary focus on metaheuristic optimization algorithms and artificial intelligence.

The scientist's work covers numerous significant topics:

  • Metaheuristic Optimization Algorithms Research
  • Advanced Multi-Objective Optimization Algorithms
  • Evolutionary Algorithms and Applications
  • Scheduling and Optimization Algorithms
  • Advanced Neural Network Applications
  • Advanced Manufacturing and Logistics Optimization
  • AI in cancer detection

Frequent publication venues for Gai-Ge Wang include:

  • Mathematics
  • Knowledge-Based Systems
  • Information Sciences
  • Expert Systems with Applications
  • Computers in Biology and Medicine

Collaborations have involved several researchers, with the most frequent co-authors being:

  • Yong Wang
  • Junyu Dong
  • Juan Li
  • Dunwei Gong
  • Muwei Jian

Recent published papers highlight a range of research topics and venues:

  • "ConvUNeXt: An efficient convolution neural network for medical image segmentation" (2022), Knowledge-Based Systems
  • "Solving Fuzzy Job-Shop Scheduling Problem Using DE Algorithm Improved by a Selection Mechanism" (2020), IEEE Transactions on Fuzzy Systems
  • "Solving Multiobjective Fuzzy Job-Shop Scheduling Problem by a Hybrid Adaptive Differential Evolution Algorithm" (2022), IEEE Transactions on Industrial Informatics
  • "A Survey of Learning-Based Intelligent Optimization Algorithms" (2021), Archives of Computational Methods in Engineering
  • "Enhancing MOEA/D with information feedback models for large-scale many-objective optimization" (2020), Information Sciences

The scientist's subfields of study further delineate the focus areas:

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Industrial and Manufacturing Engineering
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Best Publications

  • Monarch butterfly optimization

    Gai-Ge Wang;Gai-Ge Wang;Suash Deb;Zhihua Cui

  • Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems

    Gai-Ge Wang;Gai-Ge Wang;Gai-Ge Wang

  • Elephant Herding Optimization

    Gai-Ge Wang;Suash Deb;Leandro dos S. Coelho

  • Chaotic Krill Herd algorithm

    Gai-Ge Wang;Lihong Guo;Amir Hossein Gandomi;Guo-sheng Hao

  • Detection of Malicious Code Variants Based on Deep Learning

    Zhihua Cui;Fei Xue;Xingjuan Cai;Yang Cao

  • A Novel Hybrid Bat Algorithm with Harmony Search for Global Numerical Optimization

    Gaige Wang;Lihong Guo

  • A new metaheuristic optimisation algorithm motivated by elephant herding behaviour

    Gai-Ge Wang;Suash Deb;Xiao-Zhi Gao;Leandro Dos Santos Coelho

  • Solving Multiobjective Fuzzy Job-Shop Scheduling Problem by a Hybrid Adaptive Differential Evolution Algorithm

    Unknown

  • A novel oriented cuckoo search algorithm to improve DV-Hop performance for cyberphysical systems

    Zhihua Cui;Bin Sun;Gaige Wang;Yu Xue

  • Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems

    Gai Ge Wang;Suash Deb;Leandro Dos Santos Coelho

  • Improving Metaheuristic Algorithms With Information Feedback Models

    Gai-Ge Wang;Ying Tan

  • Hybridizing harmony search algorithm with cuckoo search for global numerical optimization

    Gai-Ge Wang;Amir H. Gandomi;Xiangjun Zhao;Hai Cheng Chu

  • An effective krill herd algorithm with migration operator in biogeography-based optimization

    Gai-Ge Wang;Amir H. Gandomi;Amir H. Alavi

  • Solving Fuzzy Job-Shop Scheduling Problem Using DE Algorithm Improved by a Selection Mechanism

    Da Gao;Gai-Ge Wang;Witold Pedrycz

  • Stud krill herd algorithm

    Gai-Ge Wang;Amir H. Gandomi;Amir H. Alavi

  • Chaotic cuckoo search

    Gai-Ge Wang;Suash Deb;Amir H. Gandomi;Zhaojun Zhang

  • Incorporating mutation scheme into krill herd algorithm for global numerical optimization

    Gaige Wang;Lihong Guo;Heqi Wang;Hong Duan

  • Behavior of crossover operators in NSGA-III for large-scale optimization problems

    Jiao-Hong Yi;Jiao-Hong Yi;Li-Ning Xing;Gai-Ge Wang;Junyu Dong

  • Binary optimization using hybrid particle swarm optimization and gravitational search algorithm

    Seyedali Mirjalili;Gai-Ge Wang;Leandro Dos Coelho

  • A Survey of Learning-Based Intelligent Optimization Algorithms

    Wei Li;Gai-Ge Wang;Amir H. Gandomi

  • High Performance Computing for Cyber Physical Social Systems by Using Evolutionary Multi-Objective Optimization Algorithm

    Gai-Ge Wang;Xingjuan Cai;Zhihua Cui;Geyong Min

  • Three-dimensional path planning for UCAV using an improved bat algorithm

    Gai Ge Wang;Gai Ge Wang;Haicheng Eric Chu;Seyedali Mirjalili

  • Hybrid krill herd algorithm with differential evolution for global numerical optimization

    Gai-Ge Wang;Amir H. Gandomi;Amir H. Alavi;Guo-Sheng Hao

Frequent Co-Authors

Amir H. Alavi
Amir H. Alavi University of Pittsburgh
Amir H. Gandomi
Amir H. Gandomi University of Technology Sydney
Suash Deb
Suash Deb C. V. Raman Global University
Zhihua Cui
Zhihua Cui Taiyuan University of Science and Technology
Junyu Dong
Junyu Dong Ocean University of China
Witold Pedrycz
Witold Pedrycz University of Alberta
Leandro dos Santos Coelho
Leandro dos Santos Coelho Federal University of Paraná
Xiao-Zhi Gao
Xiao-Zhi Gao University of Eastern Finland
Jinjun Chen
Jinjun Chen Swinburne University of Technology
Wei-Chang Yeh
Wei-Chang Yeh National Tsing Hua University

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