Bilel Derbel

422 total citations
26 papers, 134 citations indexed

About

Bilel Derbel is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Management Science and Operations Research. According to data from OpenAlex, Bilel Derbel has authored 26 papers receiving a total of 134 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Artificial Intelligence, 18 papers in Computational Theory and Mathematics and 5 papers in Management Science and Operations Research. Recurrent topics in Bilel Derbel's work include Metaheuristic Optimization Algorithms Research (22 papers), Advanced Multi-Objective Optimization Algorithms (18 papers) and Evolutionary Algorithms and Applications (13 papers). Bilel Derbel is often cited by papers focused on Metaheuristic Optimization Algorithms Research (22 papers), Advanced Multi-Objective Optimization Algorithms (18 papers) and Evolutionary Algorithms and Applications (13 papers). Bilel Derbel collaborates with scholars based in France, Japan and Hong Kong. Bilel Derbel's co-authors include Arnaud Liefooghe, Sebástien Vérel, Hernán Aguirre, Kiyoshi Tanaka, Fabio Daolio, Kyoung‐Jae Won, Byung‐Woo Hong, Ke Li, Qingfu Zhang and Luís Paquete and has published in prestigious journals such as SHILAP Revista de lepidopterología, European Journal of Operational Research and IEEE Transactions on Evolutionary Computation.

In The Last Decade

Bilel Derbel

22 papers receiving 133 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Bilel Derbel France 5 82 72 15 15 10 26 134
Chao Bian China 9 141 1.7× 108 1.5× 8 0.5× 12 0.8× 7 0.7× 22 184
Ishan Durugkar United States 5 73 0.9× 21 0.3× 10 0.7× 29 1.9× 12 1.2× 9 117
V. K. Panchal India 6 89 1.1× 23 0.3× 7 0.5× 36 2.4× 4 0.4× 37 157
Martin Schütz Switzerland 4 55 0.7× 44 0.6× 10 0.7× 10 0.7× 9 0.9× 7 92
Víctor Suárez-Paniagua Spain 8 174 2.1× 20 0.3× 7 0.5× 16 1.1× 6 0.6× 16 257
G. Sivakumar India 6 101 1.2× 29 0.4× 4 0.3× 12 0.8× 5 0.5× 21 155
Luona Wei China 9 104 1.3× 13 0.2× 48 3.2× 19 1.3× 14 1.4× 13 283
Hubert Zarzycki Poland 5 49 0.6× 28 0.4× 7 0.5× 6 0.4× 4 0.4× 8 75
Liam Li 2 65 0.8× 10 0.1× 8 0.5× 30 2.0× 7 0.7× 3 127
Anabela Simões Portugal 9 123 1.5× 77 1.1× 16 1.1× 3 0.2× 9 0.9× 16 150

Countries citing papers authored by Bilel Derbel

Since Specialization
Citations

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

Fields of papers citing papers by Bilel Derbel

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bilel Derbel. 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 Bilel Derbel. The network helps show where Bilel Derbel may publish in the future.

Co-authorship network of co-authors of Bilel Derbel

This figure shows the co-authorship network connecting the top 25 collaborators of Bilel Derbel. A scholar is included among the top collaborators of Bilel Derbel 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 Bilel Derbel. Bilel Derbel 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.
Derbel, Bilel, et al.. (2024). Large-scale and cooperative graybox parallel optimization on the supercomputer Fugaku. Journal of Parallel and Distributed Computing. 191. 104921–104921.
2.
Chicano, Francisco, Bilel Derbel, & Sebástien Vérel. (2023). Fourier Transform-based Surrogates for Permutation Problems. Proceedings of the Genetic and Evolutionary Computation Conference. 275–283. 1 indexed citations
3.
Santana, Roberto, et al.. (2022). Multi-objective NK landscapes with heterogeneous objectives. Proceedings of the Genetic and Evolutionary Computation Conference. 502–510. 4 indexed citations
4.
Derbel, Bilel, et al.. (2022). Drils revisited. Proceedings of the Genetic and Evolutionary Computation Conference. 204–212. 1 indexed citations
5.
Derbel, Bilel, et al.. (2021). A graph coloring based parallel hill climber for large-scale NK-landscapes. Proceedings of the Genetic and Evolutionary Computation Conference. 216–224. 1 indexed citations
6.
Paquete, Luís, et al.. (2021). On the design and anytime performance of indicator-based branch and bound for multi-objective combinatorial optimization. Proceedings of the Genetic and Evolutionary Computation Conference. 234–242. 2 indexed citations
7.
Aguirre, Hernán, et al.. (2021). Understanding Population Dynamics in Multi- and Many-Objective Evolutionary Algorithms for High-Resolution Approximations. SHILAP Revista de lepidopterología. 2021. 1–16. 1 indexed citations
8.
Derbel, Bilel, et al.. (2020). Surrogate-assisted multi-objective combinatorial optimization based on decomposition and walsh basis. HAL (Le Centre pour la Communication Scientifique Directe). 542–550. 3 indexed citations
9.
Derbel, Bilel, et al.. (2020). Designing parallelism in surrogate-assisted multiobjective optimization based on decomposition. HAL (Le Centre pour la Communication Scientifique Directe). 462–470. 3 indexed citations
10.
Derbel, Bilel, et al.. (2019). Surrogate-assisted multiobjective optimization based on decomposition. Proceedings of the Genetic and Evolutionary Computation Conference. 507–515. 6 indexed citations
11.
Liefooghe, Arnaud, Fabio Daolio, Sebástien Vérel, et al.. (2019). Landscape-Aware Performance Prediction for Evolutionary Multiobjective Optimization. IEEE Transactions on Evolutionary Computation. 24(6). 1063–1077. 47 indexed citations
12.
Derbel, Bilel, et al.. (2019). A Parallel Tabu Search for the Large-scale Quadratic Assignment Problem. 7 indexed citations
13.
Aguirre, Hernán, Saúl Zapotecas–Martínez, Arnaud Liefooghe, et al.. (2018). Pareto dominance-based MOEAs on problems with difficult pareto set topologies. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 189–190. 3 indexed citations
14.
Derbel, Bilel, Arnaud Liefooghe, Qingfu Zhang, et al.. (2018). A set-oriented MOEA/D. Proceedings of the Genetic and Evolutionary Computation Conference. 617–624. 1 indexed citations
15.
Aguirre, Hernán, et al.. (2018). Studying MOEAs dynamics and their performance using a three compartmental model. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 191–192.
16.
Zhang, Qingfu, et al.. (2018). Parallel pareto local search revisited. Proceedings of the Genetic and Evolutionary Computation Conference. 753–760. 1 indexed citations
17.
Zhang, Qingfu, et al.. (2017). A Parallel Tabu Search for the Unconstrained Binary Quadratic Programming problem. 557–564. 2 indexed citations
18.
Aguirre, Hernán, et al.. (2017). Closed state model for understanding the dynamics of MOEAs. Proceedings of the Genetic and Evolutionary Computation Conference. 609–616. 2 indexed citations
19.
Derbel, Bilel & Pierre‐Marie Preux. (2015). Simultaneous Optimistic Optimization on the Noiseless BBOB Testbed. HAL (Le Centre pour la Communication Scientifique Directe). 1 indexed citations
20.
Derbel, Bilel, et al.. (2014). Distributed localized bi-objective search. European Journal of Operational Research. 239(3). 731–743. 1 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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