Mohammed Azmi Al-Betar

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About

Mohammed Azmi Al-Betar is a prominent figure in computational intelligence, whose research focuses primarily on metaheuristic optimization algorithms, particularly the bat-inspired algorithm, and their applications across engineering and data science. His most cited work, "Recent advances of bat-inspired algorithm, its versions and applications" (2022), with 37 citations, provides a comprehensive survey that systematically categorizes the algorithm's variants, hybridizations, and real-world uses, from feature selection to scheduling problems. Al-Betar's major contribution lies in advancing swarm intelligence by refining and extending the bat algorithm's capabilities, addressing its limitations in convergence speed and solution accuracy. His impact is evident in the growing adoption of these methods in fields like renewable energy, medical diagnosis, and software engineering. Beyond this flagship paper, his broader portfolio includes influential studies on grey wolf optimization and harmony search, collectively garnering hundreds of citations. Al-Betar's work not only enriches theoretical foundations but also offers practical, scalable solutions for complex optimization challenges, making him a key reference for researchers and students seeking to harness nature-inspired algorithms for real-world problem-solving.