Yali Wu

451 total citations
26 papers, 167 citations indexed

About

Yali Wu is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Industrial and Manufacturing Engineering. According to data from OpenAlex, Yali Wu has authored 26 papers receiving a total of 167 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 8 papers in Computational Theory and Mathematics and 6 papers in Industrial and Manufacturing Engineering. Recurrent topics in Yali Wu's work include Metaheuristic Optimization Algorithms Research (7 papers), Scheduling and Optimization Algorithms (5 papers) and Advanced Multi-Objective Optimization Algorithms (4 papers). Yali Wu is often cited by papers focused on Metaheuristic Optimization Algorithms Research (7 papers), Scheduling and Optimization Algorithms (5 papers) and Advanced Multi-Objective Optimization Algorithms (4 papers). Yali Wu collaborates with scholars based in China, United States and Hong Kong. Yali Wu's co-authors include Yuhui Shi, Lixia Xie, Ge Liu, Xiaoping Guo, Ding Liu, Qiaoyong Jiang, Yanxi Yang, Junhu Wang, Bo Li and Junhu Wang and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Physica A Statistical Mechanics and its Applications.

In The Last Decade

Yali Wu

25 papers receiving 163 citations

Peers

Yali Wu
Yali Wu
Citations per year, relative to Yali Wu Yali Wu (= 1×) peers Nibedan Panda

Countries citing papers authored by Yali Wu

Since Specialization
Citations

This map shows the geographic impact of Yali Wu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yali Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yali Wu more than expected).

Fields of papers citing papers by Yali Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yali Wu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yali Wu. The network helps show where Yali Wu may publish in the future.

Co-authorship network of co-authors of Yali Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Yali Wu. A scholar is included among the top collaborators of Yali Wu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yali Wu. Yali Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Feng, Ruicheng, et al.. (2025). Identify influential nodes in directed networks: A neighborhood entropy-based method. Chaos Solitons & Fractals. 197. 116444–116444.
2.
Wu, Yali, et al.. (2024). An active learning framework using deep Q-network for zero-day attack detection. Computers & Security. 139. 103713–103713. 10 indexed citations
4.
Wu, Yali, et al.. (2024). Structural and empirical knowledge driven multi-objective evolutionary algorithm for urban drainage system design. Expert Systems with Applications. 249. 123461–123461. 2 indexed citations
5.
Liu, Ding, et al.. (2023). Two‐Stage Hybrid Optimization Algorithm for Silicon Single Crystal Batch Scheduling Problem under Fuzzy Processing Time. Mathematical Problems in Engineering. 2023(1). 3 indexed citations
6.
Jiang, Qiaoyong, et al.. (2023). A regularity model-based multi-objective estimation of distribution memetic algorithm with auto-controllable population diversity. Memetic Computing. 15(1). 45–70. 2 indexed citations
7.
Wu, Yali, et al.. (2023). Identify influential nodes in complex networks: A k-orders entropy-based method. Physica A Statistical Mechanics and its Applications. 632. 129302–129302. 7 indexed citations
8.
Liu, Ding, et al.. (2022). An Improved DE Algorithm for Solving Multi-Furnace Optimal Scheduling of Single Crystal Silicon Production. International Journal of Pattern Recognition and Artificial Intelligence. 37(2). 2 indexed citations
9.
10.
Liu, Ding, et al.. (2022). Multi-furnace optimization in silicon single crystal production plants by power load scheduling. Journal of Process Control. 117. 1–13. 4 indexed citations
11.
Jiang, Qiaoyong, et al.. (2021). A Tristage Adaptive Biased Learning for Artificial Bee Colony. Discrete Dynamics in Nature and Society. 2021. 1–22. 1 indexed citations
12.
Wu, Yali, et al.. (2019). Many-Objective Brain Storm Optimization Algorithm. IEEE Access. 7. 186572–186586. 9 indexed citations
14.
Wu, Yali, Ge Liu, Xiaoping Guo, Yuhui Shi, & Lixia Xie. (2016). A self-adaptive chaos and Kalman filter-based particle swarm optimization for economic dispatch problem. Soft Computing. 21(12). 3353–3365. 21 indexed citations
15.
Shi, Yuhui, et al.. (2013). Multi-Objective Optimization Based on Brain Storm Optimization Algorithm. International Journal of Swarm Intelligence Research. 4(3). 1–21. 48 indexed citations
16.
Wu, Yali, et al.. (2012). An improved multi-objective cultural algorithm based on particle swarm optimization. Kongzhi yu juece. 27(8). 1127–1132. 2 indexed citations
19.
Wu, Yali, et al.. (2003). Modeling and simulation of hybrid dynamical systems with generalized differential Petri nets. 736. 789–794. 4 indexed citations
20.
Wu, Yali, et al.. (2002). Modeling probabilistic logic using Petri nets. 2. 864–869. 7 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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