Fengxuan Wu

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Fengxuan Wu has made significant contributions to the field of swarm intelligence and metaheuristic optimization, with a particular focus on enhancing the performance of nature-inspired algorithms. Their most notable work, the "Improved Sparrow Search Algorithm Based on Iterative Local Search" (2021, 56 citations), addresses critical limitations in the original sparrow search algorithm, including poor utilization of current individuals and ineffective search strategies. By integrating iterative local search mechanisms, Wu's improved algorithm demonstrates superior performance on 23 basic benchmark functions and the challenging CEC 2017 test suite, effectively mitigating the problem of premature convergence. This work has become a key reference for researchers seeking to enhance exploration-exploitation balance in swarm-based optimization. Wu's research is characterized by a rigorous approach to algorithmic refinement, combining theoretical analysis with extensive empirical validation. Their contributions have practical implications for solving complex engineering optimization problems, and their work continues to influence the development of more robust and efficient metaheuristic algorithms. With growing citation impact, Wu is establishing themselves as an emerging voice in computational intelligence and optimization methodology.