Papers
12
Total Citations
1,128
H-Index
8
About
Laith Abualigah is a prolific computational intelligence researcher whose work spans metaheuristic optimization, swarm intelligence, clustering algorithms, and autonomous robotics. He is perhaps best known for his landmark 2022 comprehensive survey of clustering algorithms, which has amassed an remarkable 869 citations, establishing him as a leading authority on machine learning taxonomies and state-of-the-art algorithmic frameworks. His research consistently bridges theoretical optimization with real-world application, particularly in the domain of multi-robot and multi-agent systems, where he has developed innovative hybrid frameworks such as the Coordinated Multi-Robot Exploration Aquila Optimizer (CME-AO) and bio-inspired path planning strategies for autonomous ground robots. Abualigah's contributions demonstrate a sophisticated integration of nature-inspired algorithms — including Aquila Optimization, Whale Optimization, and Particle Swarm Optimization — with complex robotic exploration challenges in obstacle-cluttered environments. His more recent work addresses scalability limitations in high-dimensional optimization through novel group-based PSO strategies. With a growing body of highly cited interdisciplinary publications, Abualigah has firmly positioned himself as an influential voice shaping the future of intelligent systems, autonomous robotics, and evolutionary computation research.
Research Focus
Key Achievements
Top Papers
- 1
- 2Multi-Robot Space Exploration: An Augmented Arithmetic Approach67 citations · 2021
- 3A Centralized Strategy for Multi-Agent Exploration56 citations · 2022
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- 7Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective10 citations · 2023
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- 10Adaptive aquila optimizer for centralized mapping and exploration3 citations · 2024