Sanaz Mostaghim

4.5k total citations · 1 hit paper
148 papers, 2.3k citations indexed

About

Sanaz Mostaghim is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Networks and Communications. According to data from OpenAlex, Sanaz Mostaghim has authored 148 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 79 papers in Artificial Intelligence, 65 papers in Computational Theory and Mathematics and 23 papers in Computer Networks and Communications. Recurrent topics in Sanaz Mostaghim's work include Advanced Multi-Objective Optimization Algorithms (61 papers), Metaheuristic Optimization Algorithms Research (53 papers) and Evolutionary Algorithms and Applications (34 papers). Sanaz Mostaghim is often cited by papers focused on Advanced Multi-Objective Optimization Algorithms (61 papers), Metaheuristic Optimization Algorithms Research (53 papers) and Evolutionary Algorithms and Applications (34 papers). Sanaz Mostaghim collaborates with scholars based in Germany, United Kingdom and Australia. Sanaz Mostaghim's co-authors include Jürgen Teich, Heiner Zille, Hisao Ishibuchi, Yusuke Nojima, Hartmut Schmeck, Sabine Helwig, Juergen Branke, Christian Borgelt, Matthias Steinbrecher and Rudolf Kruse and has published in prestigious journals such as PLoS ONE, Sensors and Information Sciences.

In The Last Decade

Sanaz Mostaghim

129 papers receiving 2.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO)

2004 479 citations Sanaz Mostaghim, Jürgen Teich profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sanaz Mostaghim Germany	23	1.4k	1.2k	302	250	224	148	2.3k
Yinan Guo China	25	1.2k 0.8×	797 0.7×	298 1.0×	175 0.7×	120 0.5×	110	2.1k
Matej Črepinšek Slovenia	21	1.6k 1.2×	854 0.7×	201 0.7×	211 0.8×	202 0.9×	45	2.5k
Chaoli Sun China	21	1.6k 1.2×	1.5k 1.3×	185 0.6×	173 0.7×	167 0.7×	73	2.3k
Jinghui Zhong China	28	1.9k 1.4×	1.2k 1.0×	224 0.7×	191 0.8×	315 1.4×	130	2.9k
Shi-Zheng Zhao Singapore	10	2.1k 1.5×	2.1k 1.8×	346 1.1×	251 1.0×	138 0.6×	16	3.1k
Saber Elsayed Australia	32	1.9k 1.4×	1.4k 1.2×	289 1.0×	422 1.7×	178 0.8×	124	2.9k
Mostafa Z. Ali Jordan	25	1.9k 1.4×	1.2k 1.0×	299 1.0×	477 1.9×	122 0.5×	93	2.9k
Shih-Hsi Liu United States	14	1.3k 1.0×	717 0.6×	170 0.6×	196 0.8×	173 0.8×	40	2.0k
Uday K. Chakraborty United States	23	1.6k 1.2×	992 0.8×	356 1.2×	482 1.9×	242 1.1×	58	2.9k
Guangming Lin China	3	2.4k 1.7×	1.4k 1.2×	383 1.3×	367 1.5×	161 0.7×	10	3.0k

Countries citing papers authored by Sanaz Mostaghim

Since Specialization

Citations

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

Fields of papers citing papers by Sanaz Mostaghim

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sanaz Mostaghim

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

All Works

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20 of 20 papers shown

1.

Dubé, Monique G., et al.. (2025). Towards Swarms of Long Heavy Articulated Vehicles. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 111–112.

2.

Islam, Saiful, Sanaz Mostaghim, & Michael Hartmann. (2024). A Survey on Multi-Objective Optimization in Microgrid Systems. 1–8. 1 indexed citations

3.

Mostaghim, Sanaz, et al.. (2024). Many-option collective decision making: discrete collective estimation in large decision spaces. Swarm Intelligence. 18(2-3). 215–241. 1 indexed citations

4.

Mostaghim, Sanaz, et al.. (2024). Optimized Drug Design using Multi-Objective Evolutionary Algorithms with SELFIES. 1–8.

5.

Mostaghim, Sanaz, et al.. (2023). A Generalized Circular Supply Chain Problem for Multi-objective Evolutionary Algorithms. Fraunhofer-Publica (Fraunhofer-Gesellschaft). 355–358.

6.

Mostaghim, Sanaz, et al.. (2023). Landscape Analysis of Multi-objective Control of Fluidized Beds. 48. 1950–1955.

7.

Mostaghim, Sanaz, et al.. (2023). Multi-objective Multiplexer Decision Making Benchmark Problem. elib (German Aerospace Center). 1676–1683.

8.

Mostaghim, Sanaz, et al.. (2023). Interactive Role Mining Including Expert Knowledge into Evolutionary Algorithms. 2 indexed citations

9.

Mostaghim, Sanaz, et al.. (2023). Dynamic Optimization of Role Concepts for Role-Based Access Control Using Evolutionary Algorithms. SN Computer Science. 4(4). 2 indexed citations

10.

Weise, Jens, et al.. (2020). MOSAIK: A Formal Model for Self-Organizing Manufacturing Systems. IEEE Pervasive Computing. 20(1). 9–18. 6 indexed citations

11.

Steup, Christoph, et al.. (2020). Enhancing Resilience in IoT Networks using Organic Computing.. GI-Jahrestagung. 1205–1214. 2 indexed citations

12.

Henkel, Christian, et al.. (2020). Ant Colony Optimization based Multi-Robot Planner for Combined Task Allocation and Path Finding. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 487–493. 10 indexed citations

13.

Beck, Robert E., et al.. (2020). Survey into predictive key performance indicator analysis from data mining perspective. 476–483. 3 indexed citations

14.

Mostaghim, Sanaz, et al.. (2020). How cognitive and environmental constraints influence the reliability of simulated animats in groups. PLoS ONE. 15(2). e0228879–e0228879. 5 indexed citations

15.

Mostaghim, Sanaz, et al.. (2018). Understanding Collective Decision-Making in Natural Swarms. 3. 1563–1570. 1 indexed citations

16.

Mostaghim, Sanaz, et al.. (2017). HTN fighter: Planning in a highly-dynamic game. 189–194. 9 indexed citations

17.

Zille, Heiner, et al.. (2017). Dynamic Distance Minimization Problems for dynamic multi-objective optimization. 952–959. 7 indexed citations

18.

Mostaghim, Sanaz, et al.. (2009). Online and onboard evolution of robotic behavior using finite state machines. Adaptive Agents and Multi-Agents Systems. 1325–1326. 6 indexed citations

19.

Ireland, David, Andrew Lewis, Sanaz Mostaghim, & Junwei Lu. (2006). Hybrid Particle Guide Selection Methods in Multi-Objective Particle Swarm Optimization. 116–116. 10 indexed citations

20.

Mostaghim, Sanaz & Jürgen Teich. (2004). Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO). 26–33. 479 indexed citations breakdown →

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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