World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
51
Citations
8713
World Ranking
5413
National Ranking
70

Shangce Gao 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 Shangce Gao 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: 269 publications — 67th percentile

67% of scientists in this discipline score the same or lower.

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

Shangce Gao 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 Shangce Gao 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: 51 D-Index — 63rd percentile

63% of scientists in this discipline score the same or lower.

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

Overview

Shangce Gao is affiliated with the University of Toyama in Japan and has an extensive research record primarily in the fields of Computer Science and Engineering. Their research contributions span various subfields, including Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Theory and Mathematics, Electrical and Electronic Engineering, and Control and Systems Engineering.

The scientist's work largely focuses on metaheuristic optimization algorithms, advanced multi-objective optimization algorithms, evolutionary algorithms, and their applications. Additional research interests include neural networks, energy load and power forecasting, human pose and action recognition, and machine learning with extreme learning machines (ELM).

Shangce Gao has published frequently in several academic venues, with notable publication counts in:

  • Knowledge-Based Systems
  • IEICE Transactions on Information and Systems
  • IEEE/CAA Journal of Automatica Sinica
  • Applied Soft Computing
  • IEEE Access

They have collaborated with various frequent coauthors, including:

  • Zhenyu Lei
  • Jiujun Cheng
  • Yirui Wang
  • Yuki Todo
  • Haichuan Yang

Selected recent papers by Shangce Gao highlight significant contributions to optimization and modeling:

  • A state-of-the-art differential evolution algorithm for parameter estimation of solar photovoltaic models, 2021, Energy Conversion and Management

Other notable papers in related areas from the broader context of the field include works by coauthors and peers such as:

  • Complex-Valued Neural Networks: A Comprehensive Survey, 2022, IEEE/CAA Journal of Automatica Sinica
  • A multi-layered gravitational search algorithm for function optimization and real-world problems, 2021, IEEE/CAA Journal of Automatica Sinica
  • A seasonal-trend decomposition-based dendritic neuron model for financial time series prediction, 2021, Applied Soft Computing
  • An aggregative learning gravitational search algorithm with self-adaptive gravitational constants, 2020, Expert Systems with Applications

Best Publications

  • Dendritic Neuron Model With Effective Learning Algorithms for Classification, Approximation, and Prediction

    Shangce Gao;Mengchu Zhou;Yirui Wang;Jiujun Cheng

  • Routing in Internet of Vehicles: A Review

    Jiujun Cheng;Junlu Cheng;Mengchu Zhou;Fuqiang Liu

  • Chaotic Local Search-Based Differential Evolution Algorithms for Optimization

    Shangce Gao;Yang Yu;Yirui Wang;Jiahai Wang

  • Complex-Valued Neural Networks: A Comprehensive Survey

    Unknown

  • Ant colony optimization with clustering for solving the dynamic location routing problem

    Shangce Gao;Yirui Wang;Jiujun Cheng;Yasuhiro Inazumi

  • A state-of-the-art differential evolution algorithm for parameter estimation of solar photovoltaic models

    Shangce Gao;Kaiyu Wang;Sichen Tao;Ting Jin

  • Financial time series prediction using a dendritic neuron model

    Tianle Zhou;Shangce Gao;Jiahai Wang;Chaoyi Chu

  • A multi-layered gravitational search algorithm for function optimization and real-world problems

    Yirui Wang;Shangce Gao;Mengchu Zhou;Yang Yu

  • CBSO: a memetic brain storm optimization with chaotic local search

    Yang Yu;Shangce Gao;Shi Cheng;Yirui Wang

  • Gravitational search algorithm combined with chaos for unconstrained numerical optimization

    Shangce Gao;Catherine Vairappan;Yan Wang;Qiping Cao

  • A hierarchical gravitational search algorithm with an effective gravitational constant

    Yirui Wang;Yang Yu;Shangce Gao;Haiyu Pan

  • A seasonal-trend decomposition-based dendritic neuron model for financial time series prediction

    Houtian He;Shangce Gao;Ting Jin;Syuhei Sato

  • A review of applications of artificial intelligent algorithms in wind farms

    Yirui Wang;Yang Yu;Shuyang Cao;Xingyi Zhang

  • An aggregative learning gravitational search algorithm with self-adaptive gravitational constants

    Zhenyu Lei;Shangce Gao;Shubham Gupta;Jiujun Cheng

  • Information-Theory-based Nondominated Sorting Ant Colony Optimization for Multiobjective Feature Selection in Classification

    Unknown

  • An approximate logic neuron model with a dendritic structure

    Junkai Ji;Shangce Gao;Jiujun Cheng;Zheng Tang

  • Global optimum-based search differential evolution

    Yang Yu;Shangce Gao;Yirui Wang;Yuki Todo

  • Bi-objective Elite Differential Evolution Algorithm for Multivalued Logic Networks

    Jian Sun;Shangce Gao;Hongwei Dai;Jiujun Cheng

  • Accessibility Analysis and Modeling for IoV in an Urban Scene

    Jiujun Cheng;Guiyuan Yuan;Mengchu Zhou;Shangce Gao

  • A Connectivity-Prediction-Based Dynamic Clustering Model for VANET in an Urban Scene

    Jiujun Cheng;Guiyuan Yuan;MengChu Zhou;Shangce Gao

  • Incorporation of Solvent Effect into Multi-Objective Evolutionary Algorithm for Improved Protein Structure Prediction

    Shangce Gao;Shuangbao Song;Jiujun Cheng;Yuki Todo

  • Batch type local search-based adaptive neuro-fuzzy inference system (ANFIS) with self-feedbacks for time-series prediction

    Catherine Vairappan;Hiroki Tamura;Shangce Gao;Zheng Tang

  • A Novel Method for Detecting New Overlapping Community in Complex Evolving Networks

    Jiujun Cheng;Xiao Wu;Mengchu Zhou;Shangce Gao

Frequent Co-Authors

MengChu Zhou
MengChu Zhou New Jersey Institute of Technology
Jun S. Liu
Jun S. Liu Harvard University
Ji-Xiang Du
Ji-Xiang Du Huaqiao University
Hui Yu
Hui Yu University of Portsmouth
Shi Cheng
Shi Cheng Shaanxi Normal University
Antti Ylä-Jääski
Antti Ylä-Jääski Aalto University
Qingfu Zhang
Qingfu Zhang City University of Hong Kong
Abdullah Abusorrah
Abdullah Abusorrah King Abdulaziz University
Chen Peng
Chen Peng Shanghai University
Yuren Zhou
Yuren Zhou Sun Yat-sen University

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