Awaz Ahmed Shaban
Papers
2
Total Citations
14
H-Index
2
About
Awaz Ahmed Shaban is a rising researcher in artificial intelligence, whose work focuses on nature-inspired optimization and swarm intelligence algorithms. Her most-cited survey, "Swarm intelligence algorithms: a survey of modifications and applications" (2025, 8 citations), offers a comprehensive review of algorithms modeled on collective behaviors in nature—such as ant colonies, bird flocks, and fish schools—examining both their foundational principles and recent modifications. Building on this, her paper "Cuckoo search algorithm: overview, modifications, and applications" (2025, 6 citations) provides an in-depth analysis of the Cuckoo Search Algorithm, a metaheuristic inspired by the brood parasitism of cuckoo birds, highlighting its Levy flight mechanism for balancing global exploration and local exploitation. Together, these works establish Shaban as a key contributor to the synthesis and advancement of bio-inspired optimization techniques. Her research is particularly valuable for students and practitioners seeking a clear, structured understanding of how swarm intelligence and metaheuristic algorithms can be adapted for real-world applications, from engineering design to data science.
Research Focus
Key Achievements
Top Papers
- 1
- 2Cuckoo search algorithm: overview, modifications, and applications6 citations · 2025