Ellips Masehian

1.9k total citations
59 papers, 1.4k citations indexed

About

Ellips Masehian is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Control and Systems Engineering. According to data from OpenAlex, Ellips Masehian has authored 59 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Computer Vision and Pattern Recognition, 18 papers in Aerospace Engineering and 16 papers in Control and Systems Engineering. Recurrent topics in Ellips Masehian's work include Robotic Path Planning Algorithms (36 papers), Robotics and Sensor-Based Localization (17 papers) and Modular Robots and Swarm Intelligence (8 papers). Ellips Masehian is often cited by papers focused on Robotic Path Planning Algorithms (36 papers), Robotics and Sensor-Based Localization (17 papers) and Modular Robots and Swarm Intelligence (8 papers). Ellips Masehian collaborates with scholars based in Iran, United States and United Kingdom. Ellips Masehian's co-authors include Davoud Sedighizadeh, Hossein Akbaripour, Mohammad Reza Amin‐Naseri, Masoud Asadpour, Mostafa Sedighizadeh, Golnaz Habibi, Roya Soltani, Majid Eskandarpour, Hamed Fazlollahtabar and Mohammad Saidi‐Mehrabad and has published in prestigious journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and Artificial Intelligence.

In The Last Decade

Ellips Masehian

57 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ellips Masehian Iran 21 707 395 353 326 275 59 1.4k
Nikos C. Tsourveloudis Greece 18 686 1.0× 447 1.1× 518 1.5× 261 0.8× 169 0.6× 73 1.5k
Zheng Sun China 18 571 0.8× 383 1.0× 345 1.0× 186 0.6× 141 0.5× 70 1.2k
Elena R. Messina United States 19 371 0.5× 535 1.4× 354 1.0× 168 0.5× 292 1.1× 108 1.5k
Stephen Balakirsky United States 18 408 0.6× 364 0.9× 257 0.7× 136 0.4× 386 1.4× 97 1.2k
Roberto Sepúlveda Mexico 16 939 1.3× 629 1.6× 480 1.4× 108 0.3× 730 2.7× 57 1.8k
Oscar Montiel Mexico 20 1.1k 1.5× 670 1.7× 546 1.5× 135 0.4× 821 3.0× 75 2.1k
Nilanjan Chakraborty United States 19 363 0.5× 322 0.8× 180 0.5× 153 0.5× 240 0.9× 82 1.4k
Lucia Pallottino Italy 21 799 1.1× 729 1.8× 672 1.9× 132 0.4× 133 0.5× 94 1.8k
Tarek Sobh United States 18 299 0.4× 479 1.2× 111 0.3× 309 0.9× 185 0.7× 139 1.3k
Cosmin Copot Belgium 17 826 1.2× 801 2.0× 472 1.3× 87 0.3× 191 0.7× 101 1.6k

Countries citing papers authored by Ellips Masehian

Since Specialization
Citations

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

Fields of papers citing papers by Ellips Masehian

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ellips Masehian

This figure shows the co-authorship network connecting the top 25 collaborators of Ellips Masehian. A scholar is included among the top collaborators of Ellips Masehian 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 Ellips Masehian. Ellips Masehian 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.
Masehian, Ellips, et al.. (2025). An efficient solution to the simple assembly line balancing problem type 1 using iterated local search. Engineering Applications of Artificial Intelligence. 144. 110162–110162. 2 indexed citations
2.
Masehian, Ellips, et al.. (2023). Fitness landscape analysis of the simple assembly line balancing problem type 1. International Journal of Industrial Engineering Computations. 14(4). 589–608. 3 indexed citations
3.
Kabir, Ehsanollah, et al.. (2017). An Adaptive Sparse Algorithm for Synthesizing Note Specific Atoms by Spectrum Analysis, Applied to Music Signal Separation. Advances in Electrical and Computer Engineering. 17(2). 103–112.
4.
5.
Masehian, Ellips, et al.. (2016). Cooperative mapping of unknown environments by multiple heterogeneous mobile robots with limited sensing. Robotics and Autonomous Systems. 87. 188–218. 22 indexed citations
6.
Akbaripour, Hossein & Ellips Masehian. (2016). Semi-lazy probabilistic roadmap: a parameter-tuned, resilient and robust path planning method for manipulator robots. The International Journal of Advanced Manufacturing Technology. 89(5-8). 1401–1430. 55 indexed citations
7.
Masehian, Ellips, et al.. (2016). Characteristics of and Approaches to Flocking in Swarm Robotics. Applied Mechanics and Materials. 841. 240–249. 4 indexed citations
8.
Masehian, Ellips, et al.. (2015). Modular robotic systems: Methods and algorithms for abstraction, planning, control, and synchronization. Artificial Intelligence. 223. 27–64. 49 indexed citations
9.
Masehian, Ellips, Hossein Akbaripour, & Nasrin Mohabbati-Kalejahi. (2014). Solving the n-Queens Problem Using a Tuned Hybrid Imperialist Competitive Algorithm. The International Arab Journal of Information Technology. 11. 550–559. 4 indexed citations
10.
Eskandarpour, Majid, et al.. (2014). A reverse logistics network for recovery systems and a robust metaheuristic solution approach. The International Journal of Advanced Manufacturing Technology. 74(9-12). 1393–1406. 34 indexed citations
11.
Masehian, Ellips, et al.. (2013). PATH PLANNING OF TRACTOR-TRAILER ROBOT BY FAST MARCHING METHODE (FMM). 11(34). 31–47. 1 indexed citations
12.
Akbaripour, Hossein & Ellips Masehian. (2013). Efficient and Robust Parameter Tuning for Heuristic Algorithms. SHILAP Revista de lepidopterología. 18 indexed citations
13.
Masehian, Ellips, et al.. (2012). Poly line map extraction in sensor-based mobile robot navigation using a consecutive clustering algorithm. Robotics and Autonomous Systems. 60(8). 1078–1092. 6 indexed citations
14.
Masehian, Ellips, et al.. (2011). A linear programming approach for probabilistic robot path planning with missing information of outcomes. 18. 126–132. 1 indexed citations
15.
Masehian, Ellips, et al.. (2010). Designing Solvable Graphs for Multiple Moving Agents. SHILAP Revista de lepidopterología. 1 indexed citations
16.
Masehian, Ellips. (2010). New Heuristic Algorithms for Solving Single-Vehicle and Multi-VehicleGeneralized Traveling Salesman Problems (GTSP). SHILAP Revista de lepidopterología.
17.
Masehian, Ellips, et al.. (2010). Mobile Robot Online Motion Planning Using Generalized Voronoi Graphs. SHILAP Revista de lepidopterología. 4(5). 1–15. 6 indexed citations
18.
Masehian, Ellips & Davoud Sedighizadeh. (2010). Multi-objective robot motion planning using a particle swarm optimization model. Journal of Zhejiang University SCIENCE C. 11(8). 607–619. 32 indexed citations
19.
Masehian, Ellips & Davoud Sedighizadeh. (2010). Multi-Objective PSO- and NPSO-based Algorithms for Robot Path Planning. Advances in Electrical and Computer Engineering. 10(4). 69–76. 68 indexed citations
20.
Masehian, Ellips & Mohammad Reza Amin‐Naseri. (2004). A voronoi diagram‐visibility graph‐potential field compound algorithm for robot path planning. Journal of Robotic Systems. 21(6). 275–300. 58 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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