Maha Elarbi

562 citations
18 papers · 347 indexed · h-index 7
Topics
Metaheuristic Optimization Algorithms Research (7 papers)Advanced Multi-Objective Optimization Algorithms (7 papers)Evolutionary Algorithms and Applications (6 papers)
Partner nations
TunisiaMexicoItaly

In The Last Decade

Maha Elarbi

14 papers receiving 336 citations

Peers

Maha Elarbi
Comparison fields: 5 of 49
  • Artificial Intelligence 229
  • Computational Theory and Mathematics 218
  • Management Science and Operations Research 54
  • Information Systems 33
  • Control and Systems Engineering 29
Replace Giorgos Karafotias with:
Giorgos Karafotias Netherlands
Luis Miguel Antonio Mexico
Yuanchao Liu China
Dhish Kumar Saxena India
Luka Mernik United States
João A. Duro United Kingdom
Warren R. Scott United States
Bernd Reusch Germany
Hans-Michael Voigt Germany
Keinosuke Matsumoto Japan
Maha Elarbi relative to Giorgos Karafotias Netherlands Giorgos Karafotias's profile →
Citations per field
00.5×1.5×2.3×
Giorgos Karafotias · 1×
Citations per year

Countries citing papers authored by Maha Elarbi

Since Specialization
Citations

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

Fields of papers citing papers by Maha Elarbi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maha Elarbi

This figure shows the co-authorship network connecting the top 25 collaborators of Maha Elarbi. A scholar is included among the top collaborators of Maha Elarbi 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 Maha Elarbi. Maha Elarbi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
#WorkIndexed citations
1 0
2 14
3 0
4 2
5 0
6 2
7 1
8 3
9 0
10 15
11 23
12 33
13 1
14 17
15 221
16 9
17 1
18 5

About Maha Elarbi

Maha Elarbi is a scholar working on Software, Computational Theory and Mathematics and Signal Processing, having authored 18 papers that have together received 347 indexed citations. Recurring topics across this work include Metaheuristic Optimization Algorithms Research (7 papers), Advanced Multi-Objective Optimization Algorithms (7 papers) and Evolutionary Algorithms and Applications (6 papers). The work is most often cited by research in Computational Theory and Mathematics (218 citations), Artificial Intelligence (229 citations) and Software (21 citations). Maha Elarbi has collaborated with scholars based in Tunisia, Mexico and Italy. Frequent co-authors include Slim Bechikh, Lamjed Ben Saïd, Abhishek Gupta, Yew-Soon Ong, Carlos A. Coello Coello, Fabio Palomba and Zied Bahroun. Their work appears in journals such as Expert Systems with Applications, IEEE Transactions on Evolutionary Computation and Applied Soft Computing.

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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