Papers
1
Total Citations
4
H-Index
1
About
Yilin Yao is an emerging researcher in computational intelligence, with a primary focus on developing and enhancing metaheuristic optimization algorithms for solving complex global optimization problems. Yao’s most notable contribution is the "Improved Slime Mould Algorithm combine multiple strategies" (2024), which significantly advances the original Slime Mould Algorithm (SMA)—a stochastic search method inspired by the foraging behavior of slime moulds. By integrating multiple enhancement strategies, Yao’s work improves the algorithm’s convergence speed, accuracy, and robustness, offering a more effective tool for tackling challenging real-world optimization tasks. Although early in their career, this paper has already garnered 4 citations, signaling growing interest and impact in the optimization community. Yao’s research bridges nature-inspired computation and practical problem-solving, making their work valuable for students and researchers in fields like engineering, data science, and artificial intelligence. With a focus on algorithmic innovation, Yao is poised to contribute further to the advancement of global optimization techniques.
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Top Papers
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