Jean-Charles Créput

569 total citations
34 papers, 282 citations indexed

About

Jean-Charles Créput is a scholar working on Industrial and Manufacturing Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Jean-Charles Créput has authored 34 papers receiving a total of 282 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Industrial and Manufacturing Engineering, 14 papers in Computer Vision and Pattern Recognition and 12 papers in Artificial Intelligence. Recurrent topics in Jean-Charles Créput's work include Vehicle Routing Optimization Methods (16 papers), Metaheuristic Optimization Algorithms Research (11 papers) and Advanced Image and Video Retrieval Techniques (8 papers). Jean-Charles Créput is often cited by papers focused on Vehicle Routing Optimization Methods (16 papers), Metaheuristic Optimization Algorithms Research (11 papers) and Advanced Image and Video Retrieval Techniques (8 papers). Jean-Charles Créput collaborates with scholars based in France, China and Burkina Faso. Jean-Charles Créput's co-authors include Abderrafìâa Koukam, Amir Hajjam El Hassani, Alexandre Caminada, Vincent Hilaire, Baofeng Ji, Liang Liang, Jean-Philippe Diguet, Kunpeng Zhang, Liang Zhao and Lei Zhang and has published in prestigious journals such as Expert Systems with Applications, Sensors and IEEE Transactions on Evolutionary Computation.

In The Last Decade

Jean-Charles Créput

32 papers receiving 272 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jean-Charles Créput France 10 138 133 69 40 36 34 282
Venkatesh Pandiri India 9 228 1.7× 157 1.2× 58 0.8× 41 1.0× 39 1.1× 13 310
Quentin Cappart Canada 5 88 0.6× 93 0.7× 36 0.5× 55 1.4× 33 0.9× 13 258
Ali Fuat Alkaya Türkiye 8 218 1.6× 118 0.9× 28 0.4× 48 1.2× 46 1.3× 26 345
Noraini Mohd Razali Malaysia 4 79 0.6× 86 0.6× 33 0.5× 34 0.8× 38 1.1× 8 284
Xiaoshu Xiang China 9 114 0.8× 138 1.0× 38 0.6× 16 0.4× 107 3.0× 13 306
Orides Morandin Brazil 11 214 1.6× 58 0.4× 98 1.4× 21 0.5× 38 1.1× 50 380
Mostafa Mahi Iran 5 232 1.7× 310 2.3× 98 1.4× 70 1.8× 113 3.1× 8 464
Korhan Karabulut Türkiye 10 216 1.6× 72 0.5× 23 0.3× 44 1.1× 13 0.4× 20 309
Carmine Cerrone Italy 12 200 1.4× 59 0.4× 57 0.8× 52 1.3× 57 1.6× 27 352
Abdul Halim Malaysia 6 69 0.5× 167 1.3× 30 0.4× 25 0.6× 90 2.5× 12 338

Countries citing papers authored by Jean-Charles Créput

Since Specialization
Citations

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

Fields of papers citing papers by Jean-Charles Créput

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jean-Charles Créput. 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 Jean-Charles Créput. The network helps show where Jean-Charles Créput may publish in the future.

Co-authorship network of co-authors of Jean-Charles Créput

This figure shows the co-authorship network connecting the top 25 collaborators of Jean-Charles Créput. A scholar is included among the top collaborators of Jean-Charles Créput based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jean-Charles Créput. Jean-Charles Créput is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Abbas‐Turki, Abdeljalil, et al.. (2025). Distributed PSO for dynamic intersection management: Enhancing traffic flow and safety in connected autonomous vehicles. Expert Systems with Applications. 303. 130200–130200.
2.
Liang, Liang, Baofeng Ji, Lei Zhang, et al.. (2024). Exploring the YOLO-FT Deep Learning Algorithm for UAV-Based Smart Agriculture Detection in Communication Networks. IEEE Transactions on Network and Service Management. 21(5). 5347–5360. 17 indexed citations
3.
4.
Créput, Jean-Charles, et al.. (2022). Self-organizing maps and full GPU parallel approach to graph matching. Computer Communications. 198. 217–227. 1 indexed citations
5.
Créput, Jean-Charles, et al.. (2022). Generic parallel data structures and algorithms to GPU superpixel image segmentation. Displays. 74. 102275–102275. 7 indexed citations
6.
Créput, Jean-Charles, et al.. (2020). A Systematic Algorithm for Moving Object Detection with Application in Real-Time Surveillance. SN Computer Science. 1(2). 3 indexed citations
7.
Créput, Jean-Charles, et al.. (2020). Component-based 2-/3-dimensional nearest neighbor search based on Elias method to GPU parallel 2D/3D Euclidean Minimum Spanning Tree Problem. Applied Soft Computing. 100. 106928–106928. 5 indexed citations
8.
Créput, Jean-Charles, et al.. (2017). Parallel 2-Opt Local Search On Gpu. Zenodo (CERN European Organization for Nuclear Research). 4(3). 307–311. 3 indexed citations
9.
Sevaux, Marc, et al.. (2017). Optimizing the Cyclic K-conflict-free Shortest Path Problem in a Network-on-chip. HAL (Le Centre pour la Communication Scientifique Directe). 2(1). 1 indexed citations
10.
Créput, Jean-Charles, et al.. (2017). A massively parallel neural network approach to large-scale Euclidean traveling salesman problems. Neurocomputing. 240. 137–151. 26 indexed citations
11.
Wang, Hongjian, et al.. (2015). Massively parallel cellular matrix model for self-organizing map applications. 30. 584–587. 1 indexed citations
12.
Créput, Jean-Charles, et al.. (2009). Multi-Agent Environment for Modelling and Solving Dynamic Transport Problems. Computing and Informatics / Computers and Artificial Intelligence. 28(3). 277–298. 3 indexed citations
13.
Créput, Jean-Charles, et al.. (2009). A cooperative and self-adaptive metaheuristic for the facility location problem. 317–324. 3 indexed citations
14.
Créput, Jean-Charles, et al.. (2008). Un framework organisationnel pour la conception et l'implantation multi-agent de métaheuristiques (présentation courte).. 201–210. 1 indexed citations
15.
Créput, Jean-Charles, Abderrafìâa Koukam, & Amir Hajjam El Hassani. (2008). Self-organizing maps in evolutionary approach for the traveling salesman problem and vehicle routing problem with time windows. Journal of Information and Optimization Sciences. 29(3). 485–511. 3 indexed citations
16.
Créput, Jean-Charles & Abderrafìâa Koukam. (2008). A memetic neural network for the Euclidean traveling salesman problem. Neurocomputing. 72(4-6). 1250–1264. 48 indexed citations
17.
Créput, Jean-Charles & Abderrafìâa Koukam. (2008). The memetic self-organizing map approach to the vehicle routing problem. Soft Computing. 12(11). 1125–1141. 13 indexed citations
18.
Créput, Jean-Charles, et al.. (2008). A Coalition-Based Metaheuristic for the vehicle routing problem. 1176–1182. 14 indexed citations
19.
Créput, Jean-Charles & Abderrafìâa Koukam. (2007). Transport clustering and routing as a visual meshing process. Journal of Information and Optimization Sciences. 28(4). 573–601. 3 indexed citations
20.
Créput, Jean-Charles, et al.. (2006). Multi-agent approach to dynamic pick-up and delivery problem with uncertain knowledge about future transport demands. 71(1). 27–36. 11 indexed citations

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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Rankless by CCL
2026