Shaoqiang Yan

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About

Shaoqiang Yan has made significant contributions to the field of swarm intelligence and optimization algorithms, with a particular focus on enhancing the performance of nature-inspired metaheuristics. His most notable work, the "Improved Sparrow Search Algorithm Based on Iterative Local Search" (2021), which has garnered 56 citations, addresses critical limitations in the original sparrow search algorithm, such as poor utilization of current individual information and lack of effective search strategies. By integrating iterative local search mechanisms, Yan's improved algorithm demonstrates superior search performance on 23 basic benchmark functions and the challenging CEC 2017 test suite, effectively mitigating the problem of premature convergence. This work has been widely recognized for its practical impact on solving complex optimization problems. Yan's research is characterized by a rigorous approach to algorithm design and a deep understanding of the balance between exploration and exploitation in swarm intelligence. His contributions provide valuable insights for researchers and practitioners seeking to apply or further develop optimization algorithms in fields ranging from engineering design to machine learning.