Yuansheng Gao

Hi, it’s great to see you ~ 👋

I am currently pursuing my Ph.D. degree in Computer Science and Technology at Zhejiang University under the Senior Engineer Zonghui Wang and Professor Wenzhi Chen since 2024 🤗.

Current Research: My research focuses on Large Language Models 🤖. I am specifically exploring content safety and hallucination 😊.

Undergraduate Research: During my undergraduate years, I had the privilege of engaging in mathematical modeling and was awarded second prize🥈 in the China Undergraduate Mathematical Contest in Modeling in my sophomore year 🎉. It was during this time that I discovered my interest in Metaheuristics ✨.

Feel free to contact me if you are curious about my research or to discuss potential collaborations, including but not limited to Large Language Models and Metaheuristics! 😄 You can contact me via Email(y.gao@zju.edu.cn).

news

Mar 07, 2025	Can chaotic mappings be reconstructed into good metaheuristics? Our paper published in Applied Mathematical Modelling: Logistic-Gauss Circle Optimizer: Theory and Applications gives the answer.
Nov 27, 2024	Our SCI JCR Q1 paper 🕷 Escape After Love: Philoponella Prominens Optimizer and Its Application to 3D Path Planning, is published🎇!
Dec 01, 2023	Woohoo! I’m beyond excited to share that my first SCI JCR Q1 paper, PID-Based Search Algorithm: A Novel Metaheuristic Algorithm Based on PID Algorithm, got accepted! 🎉😄
Nov 02, 2022	🎉My first SCI paper, An Improved Deep Extreme Learning Machine to Predict the Remaining Useful Life of Lithium-ion Battery, has been accepted! 🥳
Nov 07, 2021	Woohoo🤩, we won second prize in the China Undergraduate Mathematical Contest in Modeling!

selected publications

ESWA

PID-based search algorithm: a novel metaheuristic algorithm based on PID algorithm

Yuansheng Gao^†

Expert Systems With Applications, 2023

Abs HTML

In this paper, a metaheuristic algorithm called PID-based search algorithm (PSA) is proposed for global optimization. The algorithm is based on an incremental PID algorithm that converges the entire population to an optimal state by continuously adjusting the system deviations. PSA is mathematically modeled and implemented to achieve optimization in a wide range of search spaces. PSA is used to solve CEC2017 benchmark test functions and six constrained problems. The optimization performance of PSA is verified by comparing it with seven metaheuristics proposed in recent years. The Kruskal-Wallis, Holm and Friedman tests verified the superiority of PSA in terms of statistical significance. The results show that PSA can be better balanced exploration and exploitation with strong optimization capability.