Maha Elarbi

562 total citations
18 papers, 347 citations indexed

About

Maha Elarbi is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Information Systems. According to data from OpenAlex, Maha Elarbi has authored 18 papers receiving a total of 347 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 7 papers in Computational Theory and Mathematics and 6 papers in Information Systems. Recurrent topics in Maha Elarbi's work include Metaheuristic Optimization Algorithms Research (7 papers), Advanced Multi-Objective Optimization Algorithms (7 papers) and Evolutionary Algorithms and Applications (6 papers). Maha Elarbi is often cited by papers focused on Metaheuristic Optimization Algorithms Research (7 papers), Advanced Multi-Objective Optimization Algorithms (7 papers) and Evolutionary Algorithms and Applications (6 papers). Maha Elarbi collaborates with scholars based in Tunisia, Mexico and Italy. Maha Elarbi's co-authors include Slim Bechikh, Lamjed Ben Saïd, Yew-Soon Ong, Abhishek Gupta, Carlos A. Coello Coello, Fabio Palomba and Zied Bahroun and has published in prestigious journals such as Expert Systems with Applications, IEEE Transactions on Evolutionary Computation and Applied Soft Computing.

In The Last Decade

Maha Elarbi

14 papers receiving 336 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Maha Elarbi Tunisia	7	229	218	54	33	29	18	347
Luis Miguel Antonio Mexico	8	331 1.4×	297 1.4×	47 0.9×	18 0.5×	44 1.5×	14	439
Dhish Kumar Saxena India	10	296 1.3×	336 1.5×	106 2.0×	21 0.6×	60 2.1×	22	465
Raquel Hernández Gómez Mexico	6	355 1.6×	381 1.7×	112 2.1×	19 0.6×	29 1.0×	9	488
Zefeng Chen China	12	252 1.1×	230 1.1×	49 0.9×	11 0.3×	31 1.1×	28	361
Giorgos Karafotias Netherlands	6	317 1.4×	193 0.9×	23 0.4×	15 0.5×	13 0.4×	10	400
Yicun Hua China	5	249 1.1×	229 1.1×	47 0.9×	14 0.4×	48 1.7×	8	396
Yuanchao Liu China	14	365 1.6×	295 1.4×	43 0.8×	10 0.3×	46 1.6×	42	528
Yuji Sakane Japan	8	298 1.3×	340 1.6×	73 1.4×	9 0.3×	30 1.0×	11	402
João A. Duro United Kingdom	7	201 0.9×	217 1.0×	69 1.3×	14 0.4×	64 2.2×	13	410
Luka Mernik United States	5	221 1.0×	122 0.6×	16 0.3×	11 0.3×	33 1.1×	7	371

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

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18 of 18 papers shown

Elarbi, Maha, et al.. (2025). Deep crossover schemes for genetic algorithms: Investigations on the travel salesman problem. Swarm and Evolutionary Computation. 98. 102094–102094.

Elarbi, Maha, et al.. (2022). Discretization-Based Feature Selection as a Bilevel Optimization Problem. IEEE Transactions on Evolutionary Computation. 27(4). 893–907. 14 indexed citations

Elarbi, Maha, et al.. (2022). Interval-based Cost-sensitive Classification Tree Induction as a Bi-level Optimization Problem. 1–8.

Elarbi, Maha, et al.. (2022). Handling uncertainty in SBSE: a possibilistic evolutionary approach for code smells detection. Empirical Software Engineering. 27(6).

Elarbi, Maha, et al.. (2022). A bi-level evolutionary approach for the multi-label detection of smelly classes. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 782–785. 2 indexed citations

Elarbi, Maha, et al.. (2022). Uncertainty-wise software anti-patterns detection: A possibilistic evolutionary machine learning approach. Applied Soft Computing. 129. 109620–109620. 2 indexed citations

Elarbi, Maha, et al.. (2021). A Possibilistic Evolutionary Approach to Handle the Uncertainty of Software Metrics Thresholds in Code Smells Detection. 574–585. 3 indexed citations

Elarbi, Maha, et al.. (2021). An Evolutionary Multi-objective Approach for Coordinating Supplier–Producer Conflict in Lot Sizing. International Journal of Information Technology & Decision Making. 21(2). 541–575. 1 indexed citations

Elarbi, Maha, et al.. (2021). Dealing with Label Uncertainty in Web Service Anti-patterns Detection using a Possibilistic Evolutionary Approach. 463. 347–357.

10.

Elarbi, Maha, et al.. (2021). Solving combinatorial bi-level optimization problems using multiple populations and migration schemes. Operational Research. 22(3). 1697–1735. 15 indexed citations

11.

Bechikh, Slim, et al.. (2020). Code smell detection and identification in imbalanced environments. Expert Systems with Applications. 166. 114076–114076. 23 indexed citations

12.

Elarbi, Maha, et al.. (2019). Approximating Complex Pareto Fronts With Predefined Normal-Boundary Intersection Directions. IEEE Transactions on Evolutionary Computation. 24(5). 809–823. 33 indexed citations

13.

Bechikh, Slim, et al.. (2019). A Hybrid Evolutionary Algorithm with Heuristic Mutation for Multi-objective Bi-clustering. 2323–2330. 1 indexed citations

14.

Elarbi, Maha, Slim Bechikh, & Lamjed Ben Saïd. (2018). On the importance of isolated infeasible solutions in the many-objective constrained NSGA-III. Knowledge-Based Systems. 227. 104335–104335. 17 indexed citations

15.

Elarbi, Maha, Slim Bechikh, Abhishek Gupta, Lamjed Ben Saïd, & Yew-Soon Ong. (2017). A New Decomposition-Based NSGA-II for Many-Objective Optimization. IEEE Transactions on Systems Man and Cybernetics Systems. 48(7). 1191–1210. 221 indexed citations

16.

Saïd, Lamjed Ben, et al.. (2017). Evidential learning classifier system. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 123–124. 1 indexed citations

17.

Elarbi, Maha, Slim Bechikh, & Lamjed Ben Saïd. (2017). On the importance of isolated solutions in constrained decomposition-based many-objective optimization. Proceedings of the Genetic and Evolutionary Computation Conference. 561–568. 9 indexed citations

18.

Elarbi, Maha, et al.. (2016). Solving many-objective problems using targeted search directions. 89–96. 5 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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