Ying Tan

Ying Tan is a full professor and PhD advisor at the School of Electronics Engineering and Computer Science of Peking University, and director of Computational Intelligence Laboratory at Peking University (PKU). He received his BEng, MS, and PhD from Southeast Univ., in 1985, 1988, and 1997, respectively. He is the inventor of Fireworks Algorithm (FWA). He serves as the Editor-in-Chief of International Journal of Computational Intelligence and Pattern Recognition (IJCIPR), the Associate Editor of IEEE Transaction on Cybernetics (Cyb), the Associate Editor of IEEE Transaction on Neural Networks and Learning Systems (NNLS),International Journal of Artificial Intelligence (IJAI), International Journal of Swarm Intelligence Research (IJSIR), etc. He also served as an Editor of Springer’s Lecture Notes on Computer Science (LNCS) for more than 15 volumes, and Guest Editors of several referred Journals, including Information Science, Softcomputing, Neurocomputing, Natural Computing, International Journal of Artificial Intelligence, IJSIR, B&B, Computer Journal, IEEE/ACM Transactions on Computational Biology and Bioinformatics. He is a member of Emergent Technologies Technical Committee (ETTC), Computational Intelligence Society of IEEE since 2010. He is a senior member of IEEE and ACM and a senior member of the CIE. He is the founder and chair of the ICSI International Conference series. He was the general chair of joint general chair of 1st&2nd BRICS CCI, program committee co-chair of IEEE WCCI 2014, publicity chair of IEEE SSCI 2016, etc. Prof. Ying Tan’s main interests include computational intelligence, swarm intelligence, swarm robotics, data mining, and intelligent information processing for information security. He has published more than two-hundred papers in refereed journals and conferences in these areas, and authored/co-authored ten books and fifteen book chapters, and received three invention patents.

Publications

Application of Fireworks Algorithm in Bioinformatics
Application of Fireworks Algorithm in Bioinformatics
Yuchen Zhang, Xiujuan Lei, Ying Tan. © 2024. 31 pages.
Fireworks Algorithm (FWA) has been applied to many fields in recent years, showing a strong ability to solve optimization problems. In this chapter, FWA is applied to some...
Handbook of Research on Fireworks Algorithms and Swarm Intelligence
Handbook of Research on Fireworks Algorithms and Swarm Intelligence
Ying Tan. © 2020. 471 pages.
In recent years, swarm intelligence has become a popular computational approach among researchers working on optimization problems throughout the globe. Several algorithms inside...
Recent Developments of Fireworks Algorithms
Recent Developments of Fireworks Algorithms
Ying Tan. © 2020. 41 pages.
Fireworks Algorithm has been proposed for almost 10 years. Because of its basic but profound collaborative searching manner and advantages of universal effectiveness, hundreds of...
Application of Fireworks Algorithm in Bioinformatics
Application of Fireworks Algorithm in Bioinformatics
Yuchen Zhang, Xiujuan Lei, Ying Tan. © 2020. 30 pages.
Fireworks Algorithm (FWA) has been applied to many fields in recent years, showing a strong ability to solve optimization problems. In this chapter, FWA is applied to some...
Swarm Intelligent Optimization Algorithms and Its Application in Mobile Robot Path Planning
Swarm Intelligent Optimization Algorithms and Its Application in Mobile Robot Path Planning
Xiujuan Lei, Fei Wang, Ying Tan. © 2019. 40 pages.
Mobile robot path planning is generally a kind of optimal problems, which is to find a best path of a track between a starting point to a goal point in the constraint conditions....
A Survey on Swarm Robotics
A Survey on Swarm Robotics
Ying Tan. © 2017. 43 pages.
In this chapter, the current work on swarm robotics is briefly reviewed. Swarm robotics, inspired from nature swarm, is a combination of swarm intelligence and robotic, and shows...
Swarm Intelligence for Dimensionality Reduction: How to Improve the Non-Negative Matrix Factorization with Nature-Inspired Optimization Methods
Swarm Intelligence for Dimensionality Reduction: How to Improve the Non-Negative Matrix Factorization with Nature-Inspired Optimization Methods
Andreas Janecek, Ying Tan. © 2017. 26 pages.
Low-rank approximations allow for compact representations of data with reduced storage and runtime requirements and reduced redundancy and noise. The Non-Negative Matrix...
Mobile Target Tracking of Swarm Robotics in Unknown Obstructive Environment
Mobile Target Tracking of Swarm Robotics in Unknown Obstructive Environment
Zhongyang Zheng, Ying Tan. © 2017. 27 pages.
This paper considers the problem of tracking a mobile target in an obstructive environment using a swarm of simple robots with limited sensing and communicating abilities. The...
Handbook of Research on Design, Control, and Modeling of Swarm Robotics
Handbook of Research on Design, Control, and Modeling of Swarm Robotics
Ying Tan. © 2016. 854 pages.
Studies on robotics applications have grown substantially in recent years, with swarm robotics being a relatively new area of research. Inspired by studies in swarm intelligence...
A Survey on Swarm Robotics
A Survey on Swarm Robotics
Ying Tan. © 2016. 41 pages.
In this chapter, the current work on swarm robotics is briefly reviewed. Swarm robotics, inspired from nature swarm, is a combination of swarm intelligence and robotic, and shows...
Swarm Intelligent Optimization Algorithms and Its Application in Mobile Robot Path Planning
Swarm Intelligent Optimization Algorithms and Its Application in Mobile Robot Path Planning
Xiujuan Lei, Fei Wang, Ying Tan. © 2016. 33 pages.
Mobile robot path planning is generally a kind of optimal problems, which is to find a best path of a track between a starting point to a goal point in the constraint conditions....
Mobile Target Tracking of Swarm Robotics in Unknown Obstructive Environment
Mobile Target Tracking of Swarm Robotics in Unknown Obstructive Environment
Zhongyang Zheng, Ying Tan. © 2016. 24 pages.
This paper considers the problem of tracking a mobile target in an obstructive environment using a swarm of simple robots with limited sensing and communicating abilities. The...
Swarm Intelligence for Non-Negative Matrix Factorization
Swarm Intelligence for Non-Negative Matrix Factorization
Andreas Janecek, Ying Tan. © 2013. 25 pages.
The Non-negative Matrix Factorization (NMF) is a special low-rank approximation which allows for an additive parts-based and interpretable representation of the data. This...
Particle Swarm Optimization Algorithms Inspired by Immunity-Clonal Mechanism and Their Applications to Spam Detection
Particle Swarm Optimization Algorithms Inspired by Immunity-Clonal Mechanism and Their Applications to Spam Detection
Ying Tan. © 2012. 24 pages.
Compared to conventional PSO algorithm, particle swarm optimization algorithms inspired by immunity-clonal strategies are presented for their rapid convergence, easy...
Swarm Intelligence for Non-Negative Matrix Factorization
Swarm Intelligence for Non-Negative Matrix Factorization
Andreas Janecek, Ying Tan. © 2011. 23 pages.
The Non-negative Matrix Factorization (NMF) is a special low-rank approximation which allows for an additive parts-based and interpretable representation of the data. This...
International Journal of Swarm Intelligence Research (IJSIR)
International Journal of Swarm Intelligence Research (IJSIR)
Yuhui Shi. Est. 2010.
The International Journal of Swarm Intelligence Research (IJSIR) serves as a forum for facilitating and enhancing the information sharing among swarm intelligence researchers in...
Particle Swarm Optimization Algorithms Inspired by Immunity-Clonal Mechanism and Their Applications to Spam Detection
Particle Swarm Optimization Algorithms Inspired by Immunity-Clonal Mechanism and Their Applications to Spam Detection
Ying Tan. © 2010. 23 pages.
Compared to conventional PSO algorithm, particle swarm optimization algorithms inspired by immunity-clonal strategies are presented for their rapid convergence, easy...