Amir H. Gandomi

60.2k citations
610 papers · 43.7k indexed · 41 hit papers · h-index 92

Amir H. Gandomi

586 papers receiving 42.2k citations

Hit Papers

Toward a ...49201120262016202150010001.5k2.0k

Peers

Amir H. Gandomi
Comparison fields: 5 of 222
  • Artificial Intelligence 18.9k
  • Computational Theory and Mathematics 8.2k
  • Civil and Structural Engineering 7.6k
  • Industrial and Manufacturing Engineering 2.1k
  • Computer Vision and Pattern Recognition 4.2k
Replace Xin‐She Yang with:
Xin‐She Yang United Kingdom
Sakshi Agarwal India
Amrit Pratap United States
R.C. Eberhart United States
Carlos A. Coello Coello Mexico
James Kennedy United States
David E. Goldberg United States
Andrew Lewis Australia
Jun Zhang China
Ponnuthurai Nagaratnam Suganthan Singapore
Amir H. Gandomi relative to Xin‐She Yang United Kingdom Xin‐She Yang's profile →
Citations per field
00.5×5.9×
Xin‐She Yang · 1×
Citations per year

Countries citing papers authored by Amir H. Gandomi

Since Specialization
Citations

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

Fields of papers citing papers by Amir H. Gandomi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Amir H. Gandomi. 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 Amir H. Gandomi. The network helps show where Amir H. Gandomi may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Amir H. Gandomi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Amir H. Gandomi Line = papers co-authored together Amir H. Gandomi links everyone, so they are left out of the graph.

All Works

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Deep learning modelling techniques: current progress, applications, advantages, and challengesbreakdown →
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About Amir H. Gandomi

Amir H. Gandomi is a scholar working on Civil and Structural Engineering, Computational Theory and Mathematics and Artificial Intelligence, having authored 610 papers that have together received 43.7k indexed citations. Recurring topics across this work include Metaheuristic Optimization Algorithms Research (134 papers), Advanced Multi-Objective Optimization Algorithms (103 papers), Evolutionary Algorithms and Applications (83 papers), Structural Health Monitoring Techniques (61 papers), Infrastructure Maintenance and Monitoring (40 papers), IoT and Edge/Fog Computing (34 papers), Geotechnical Engineering and Analysis (30 papers) and Topology Optimization in Engineering (28 papers). The work is most often cited by research in Artificial Intelligence (18.9k citations), Computational Theory and Mathematics (8.2k citations) and Civil and Structural Engineering (7.6k citations). Amir H. Gandomi has collaborated with scholars based in Australia, United States and Iran. Frequent co-authors include Amir H. Alavi, Seyedali Mirjalili, Xin‐She Yang, Laith Abualigah, Mohamed Abd Elaziz, Huiling Chen, Siamak Talatahari, Gai‐Ge Wang, Shahrzad Saremi and Seyedeh Zahra Mirjalili. Their work appears in journals such as SHILAP Revista de lepidopterología, Renewable and Sustainable Energy Reviews and PLoS ONE.

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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