Qiqi Liu

1.1k total citations · 2 hit papers
30 papers, 779 citations indexed

About

Qiqi Liu is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Management Science and Operations Research. According to data from OpenAlex, Qiqi Liu has authored 30 papers receiving a total of 779 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Artificial Intelligence, 20 papers in Computational Theory and Mathematics and 8 papers in Management Science and Operations Research. Recurrent topics in Qiqi Liu's work include Advanced Multi-Objective Optimization Algorithms (20 papers), Metaheuristic Optimization Algorithms Research (17 papers) and Evolutionary Algorithms and Applications (7 papers). Qiqi Liu is often cited by papers focused on Advanced Multi-Objective Optimization Algorithms (20 papers), Metaheuristic Optimization Algorithms Research (17 papers) and Evolutionary Algorithms and Applications (7 papers). Qiqi Liu collaborates with scholars based in China, Germany and United Kingdom. Qiqi Liu's co-authors include Yaochu Jin, Yicun Hua, Kuangrong Hao, Tobias Rodemann, Guo Yu, Jianping Luo, Yun Yang, Min-Rong Chen, Xia Li and Wenming Cao and has published in prestigious journals such as Information Sciences, Building and Environment and IEEE Transactions on Cybernetics.

In The Last Decade

Qiqi Liu

29 papers receiving 763 citations

Hit Papers

A Survey of Evolutionary Algorithms for Multi-Objective O... 2021 2026 2022 2024 2021 2025 50 100 150 200

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Qiqi Liu China 14 443 438 116 68 58 30 779
Gregorio Toscano‐Pulido Mexico 14 428 1.0× 508 1.2× 92 0.8× 43 0.6× 57 1.0× 41 718
Saúl Zapotecas–Martínez Mexico 16 551 1.2× 611 1.4× 118 1.0× 47 0.7× 83 1.4× 52 827
Ricardo Landa Becerra Mexico 12 639 1.4× 519 1.2× 57 0.5× 73 1.1× 87 1.5× 28 907
Tatsuya Okabe Germany 8 349 0.8× 340 0.8× 55 0.5× 72 1.1× 80 1.4× 12 757
Sunith Bandaru Sweden 16 335 0.8× 360 0.8× 70 0.6× 181 2.7× 51 0.9× 46 762
Hu Zhang China 15 469 1.1× 395 0.9× 46 0.4× 42 0.6× 37 0.6× 46 684
Jianping Luo China 15 384 0.9× 321 0.7× 69 0.6× 190 2.8× 63 1.1× 42 774
Mario Andrés Muñoz Australia 15 477 1.1× 265 0.6× 63 0.5× 91 1.3× 87 1.5× 55 857
Ziyu Hu China 18 454 1.0× 411 0.9× 57 0.5× 119 1.8× 110 1.9× 75 940
Gaoji Sun China 14 484 1.1× 291 0.7× 75 0.6× 41 0.6× 100 1.7× 35 769

Countries citing papers authored by Qiqi Liu

Since Specialization
Citations

This map shows the geographic impact of Qiqi Liu'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 Qiqi Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qiqi Liu more than expected).

Fields of papers citing papers by Qiqi Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Qiqi Liu. 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 Qiqi Liu. The network helps show where Qiqi Liu may publish in the future.

Co-authorship network of co-authors of Qiqi Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Qiqi Liu. A scholar is included among the top collaborators of Qiqi Liu 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 Qiqi Liu. Qiqi Liu 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.
Liu, Qiqi, et al.. (2025). Data-Driven Evolutionary Algorithm Based on Inductive Graph Neural Networks for Multimodal Multiobjective Optimization. IEEE Transactions on Evolutionary Computation. 30(1). 186–198. 8 indexed citations
3.
Liu, Qiqi, et al.. (2025). Unveiling differential impacts of multidimensional urban morphology on heat island effect across local climate zones: Interpretable CatBoost-SHAP machine learning model. Building and Environment. 270. 112574–112574. 20 indexed citations breakdown →
4.
Liu, Qiqi, et al.. (2024). Federated Bayesian optimization via compressed sensing. Information Sciences. 681. 121148–121148. 1 indexed citations
5.
Zhang, Qihang, Qiqi Liu, & Yunhe Wang. (2024). Self-Supervised Heterogeneous Graph Neural Network Based on Multi-Head Self-Attention. 136–141. 2 indexed citations
6.
Hua, Yicun, Qiqi Liu, & Kuangrong Hao. (2024). Adaptive normal vector guided evolutionary multi- and many-objective optimization. Complex & Intelligent Systems. 10(3). 3709–3726. 11 indexed citations
7.
Zhang, Xi, Yaochu Jin, Ping Wu, & Qiqi Liu. (2024). Dynamic Multi-Objective Home Appliance Scheduling Using Transfer Learning Based Evolutionary Algorithm. 438–444. 1 indexed citations
8.
Yang, Shuai, et al.. (2024). Adaptive and Communication-Efficient Zeroth-Order Optimization for Distributed Internet of Things. IEEE Internet of Things Journal. 11(22). 37200–37213. 14 indexed citations
9.
Luo, Jianping, Qiqi Liu, Zexuan Zhu, et al.. (2024). A New Multitask Joint Learning Framework for Expensive Multi-Objective Optimization Problems. IEEE Transactions on Emerging Topics in Computational Intelligence. 8(2). 1894–1909. 4 indexed citations
10.
Liu, Qiqi, et al.. (2023). Surrogate-Assisted Many-Objective Optimization of Building Energy Management. IEEE Computational Intelligence Magazine. 18(4). 14–28. 10 indexed citations
11.
Liu, Qiqi, et al.. (2023). Secure Federated Evolutionary Optimization—A Survey. Engineering. 34. 23–42. 4 indexed citations
12.
Liu, Qiqi, et al.. (2023). A Performance Indicator-Based Infill Criterion for Expensive Multi-/Many-Objective Optimization. IEEE Transactions on Evolutionary Computation. 27(4). 1085–1099. 32 indexed citations
13.
Yu, Guo, Lianbo Ma, Yaochu Jin, et al.. (2022). A Survey on Knee-Oriented Multiobjective Evolutionary Optimization. IEEE Transactions on Evolutionary Computation. 26(6). 1452–1472. 45 indexed citations
14.
Liu, Qiqi, Ying Zhang, Zhourui Xu, et al.. (2022). Mesoporous silica-coated silver nanoparticles as ciprofloxacin/siRNA carriers for accelerated infected wound healing. Journal of Nanobiotechnology. 20(1). 386–386. 46 indexed citations
15.
Liu, Qiqi, et al.. (2022). Coordinated Adaptation of Reference Vectors and Scalarizing Functions in Evolutionary Many-Objective Optimization. IEEE Transactions on Systems Man and Cybernetics Systems. 53(2). 763–775. 18 indexed citations
16.
Liu, Qiqi, et al.. (2022). Surrogate-assisted evolutionary optimization of expensive many-objective irregular problems. Knowledge-Based Systems. 240. 108197–108197. 19 indexed citations
17.
Yu, Guo, Yaochu Jin, Markus Olhofer, Qiqi Liu, & Wenli Du. (2021). Solution Set Augmentation for Knee Identification in Multiobjective Decision Analysis. IEEE Transactions on Cybernetics. 53(4). 2480–2493. 8 indexed citations
18.
Liu, Qiqi, et al.. (2020). An Adaptive Reference Vector-Guided Evolutionary Algorithm Using Growing Neural Gas for Many-Objective Optimization of Irregular Problems. IEEE Transactions on Cybernetics. 52(5). 2698–2711. 100 indexed citations
19.
Luo, Jianping, Yun Yang, Qiqi Liu, et al.. (2018). A new hybrid memetic multi-objective optimization algorithm for multi-objective optimization. Information Sciences. 448-449. 164–186. 26 indexed citations
20.
Luo, Jianping, Yun Yang, Xia Li, et al.. (2017). A decomposition-based multi-objective evolutionary algorithm with quality indicator. Swarm and Evolutionary Computation. 39. 339–355. 26 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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