Prof. Dr.-Ing. Qirong TANG
Tongji University, China
Biography:
Qirong TANG, who obtained his Ph.D. from University of Stuttgart, Germany, and is currently a full professor (with distinguish) at Tongji University Shanghai, China. He is the founding director of the Laboratory of Robotics and Multibody System (RMB), and the leader of the Intelligent Unmanned Systems Group. Meanwhile, he serves as the Vice Dean of School of Mechanical Engineering, and a member of Council of Tongji University. He is the holder of National high-level Talents Program and Shanghai Pujiang Scholar.
Title: Swarm Intelligence and Large-scale Heterogeneous Robotic Swarms
Abstract: Swarm intelligence and swarm robotics attract extensive attention for the past few years. However, there are still many questions deserve our questions. For examples, what are the essential mechanisms of swarm intelligence? How to make the robot physical body adapt bidirectionally? How to deal with the increase of the scale of robotic swarms? How to build such large models?
This talk will focus on swarm intelligence and swarm robotics, especially large-scale heterogeneous robotic swarms. It will introduce some recent applications of swarm intelligence on swarm robots in speaker’s group, such as collaborative operation and confrontation in complex environment with swarm robots under supervision of swarm intelligent algorithms. It will start from the contrast of kinematics and dynamics based modeling methods and machine learning based methods, to the cluster control of swarm robots, indirect communication, and ending with construction as well as application of swarm robotic platforms. The speaker would like to share with you the above recent research from his group.
Dr. Chaomin Luo
Mississippi State University, USA
Biography:
Dr. Chaomin Luo received his Ph.D. in Electrical and Computer Engineering from the Department of Electrical and Computer Engineering at University of Waterloo, Canada in 2008, his M.Sc. in Engineering Systems and Computing at University of Guelph, Canada, and his B.Eng. in Electrical Engineering from Southeast University, Nanjing, China. After he received his Ph.D., he was an Assistant Professor and then an Associate Professor, in the Department of Electrical and Computer Engineering, at the University of Detroit Mercy, Michigan, USA. He is currently an Associate Professor, Department of Electrical and Computer Engineering, at the Mississippi State University, Mississippi State, MS 39762, USA. His research interests include biologically inspired intelligence, swarm intelligence, computational intelligence, robotics, autonomous systems and control, and applied machine learning.
He was Associate Editor in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE IROS’2019). He is Tutorials Co-Chair in the 2020 IEEE Symposium Series on Computational Intelligence (IEEE SSCI’2020). Dr. Luo is selected in the Marquis Who's Who in America, 2019-2020 edition. He received the Best Paper Presentation Award at the SWORD’2007 Conference, the Best Paper Award in the IEEE International Conference on Information and Automation (IEEE ICIA’2017). He was the panelist in the Department of Defense, USA, 2015-2016, 2016-2017 NDSEG Fellowship program and panelist in 2017 NSF GRFP Panelist program.
Dr. Luo is active nationally and internationally in his research field. He was the Program Co-Chair in 2018 IEEE International Conference on Information and Automation (IEEE-ICIA’2018). He was the Plenary Session Co-Chair in the 2019 and 2018 International Conference on Swarm Intelligence, and he was the Invited Session Co-Chair in the 2017 International Conference on Swarm Intelligence. He was the General Co-Chair of the 1st IEEE International Workshop on Computational Intelligence in Smart Technologies (IEEE-CIST 2015), and Journal Special Issues Chair, IEEE 2016 International Conference on Smart Technologies (IEEE-SmarTech), Cleveland, OH, USA. Also, he was Chair and Vice Chair of IEEE SEM - Computational Intelligence Chapter and was a Chair of IEEE SEM - Computational Intelligence Chapter and Chair of Education Committee of IEEE SEM.
Dr. Luo serves as the Associate Editor of IEEE Transactions on Cognitive and Developmental Systems, Associate Editor of International journal of Robotics and Automation, Associate Editor of International Journal of Swarm Intelligence Research (IJSIR), and the Editorial Board Member of Journal of Industrial Electronics and Applications, and International Journal of Complex Systems – Computing, Sensing and Control.
He has organized and chaired several special sessions on topics of Intelligent Vehicle Systems and Bio-inspired Intelligence in reputed international conferences such as IJCNN, IEEE-SSCI, IEEE-CEC, IEEE-CASE, and IEEE-Fuzzy, etc. He has extensively published in reputed journals and conference proceedings, such as IEEE Transactions on Industrial Electronics, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on SMC, IEEE Transactions on Cybernetics, IEEE-ICRA, and IEEE-IROS, etc.
Title: New Insights into Nature Inspired Intelligence Methodologies with Applications in Robotics and Autonomous Systems
Abstract: The theme of nature inspired intelligence techniques, swarm intelligence and dynamic evolutionary optimization theory, an important embranchment of series on computational intelligence and machine learning, plays a crucial role for intelligent agents. The autonomous robot and vehicle industry have had an immense impact on our economy and society, and this trend will continue with nature inspired intelligence, swarm intelligence, techniques and dynamic evolutionary optimization theory.
A sequence of novel neural dynamics, evolutionary computation algorithms and evolutionary optimization algorithms associated with developed numerical methods, spline-based and vector-driven methods for autonomous robot navigation and mapping are proposed. From biologically motivated neural networks algorithms to evolutionary computation algorithms, nature inspired intelligence techniques and dynamic evolutionary optimization are employed to autonomous system navigation, mapping, path planning, localization and vision, in this research.
Automobile accidents account for nearly 34,000 accidental deaths, unfortunately, in the United States yearly; that number is expected to rise by 65% over the next 20 years. The objective of Advanced Driver Assistance Systems (ADAS) is to support drivers through warning to reduce the risk exposure, triggering the protection cycles to prevent from accidents. Sensor fusion, system modeling and development for ADAS by nature inspired intelligence are performed and addressed as well.
Simulation, comparison studies and experimental results of nature inspired intelligence, swarm intelligence techniques and dynamic evolutionary optimization algorithms applied for an autonomous robot and a multi-robot system demonstrate their effectiveness, efficiency and robustness of the proposed methodologies.
Prof. Gai-Ge Wang
Ocean University of China, China
Biography:
Gai-Ge Wang is an associate professor in Ocean University of China, China. His entire publications have been cited over 8800 times (Google Scholar). Fifteen and sixty-six papers are selected as Highly Cited Paper by Web of Science, and Scopus (till July, 2021), respectively. One paper is selected as “Top Articles from Outstanding S&T Journals of China-F5000 Frontrunner”. He was selected as one of “2020 Highly Cited Chinese Researchers” in computer science and technology by Elsevier. He was selected as World’s Top 2% Scientists 2020, ranked 3840 in single 2019 (ranked 30762 in 2017), and ranked 88554 in career-long citation impact. One of his papers was selected as “100 Most Influential International Academic Papers in China”, One of his paper ranks 1 in the selection of the latest high-impact publications in computer science by Chinese researchers across from Springer Nature in 2019. The latest Google h-index and i10-index are 52 and 103, respectively. He is senior member of SAISE, SCIEI, a member of IEEE, IEEE CIS, ISMOST. He served as Editorial Advisory Board Member of Communications in Computational and Applied Mathematics (CCAM), Associate Editor of IJCISIM, an Editorial Board Member of IEEE Access, Mathematics, IJBIC, Karbala International Journal of Modern Science, and Journal of Artificial Intelligence and Systems. He served as Guest Editor for many journals including Mathematics, IJBIC, FGCS, Memetic Computing and Operational Research. His research interests are swarm intelligence, evolutionary computation, and big data optimization.
Title: Improving Metaheuristic Algorithms Using Information Feedback Model
Abstract: In most metaheuristic algorithms, the individual update process does not (fully) utilize the individual information generated in previous iterations. If this useful information can be fully utilized in subsequent optimization processes, the quality of the feasible solutions produced by the algorithm will be greatly improved. Based on this, a method of reusing available information of previous individuals to guide subsequent search is proposed. In this method, the previous useful information is fed back to the individual update process, and then six information feedback models are proposed. In these models, the individuals of previous iterations are selected in a fixed or random way, and then the useful information of the selected individuals is applied to the individual update process. Then, based on the individuals generated and selected by the basic algorithm, a simple fitness weighting method is used to generate new individuals. Six different information feedback models are applied to 10 metaheuristic algorithms to generate new algorithms and verify the performance of the proposed information feedback model. Experiments show that these new algorithms are significantly better than the basic algorithms on 14 standard test functions and 10 CEC 2011 real world problems, and further prove the effectiveness of the proposed information feedback model. At the same time, the model is applied to solve many-objective optimization methods (MOEA/D and NSGA-III), and good results are achieved.
Prof. Wenjian Luo
Harbin Institute of Technology, Shenzhen, China.
Biography:
Wenjian Luo is a professor of School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China. He received the BS and PhD degrees from Department of Computer Science and Technology, University of Science and Technology of China, Hefei, China. His research interest is artificial intelligence and applications including swarm intelligence, machine learning and immune computation. He has published over 100 journal and conference papers. He currently serves as an associate editor or editorial board member for several journals including Information Sciences Journal, Swarm and Evolutionary Computation Journal, Journal of Information Security and Applications, Applied Soft Computing Journal and Complex & Intelligent Systems Journal. Currently he also serves as the chair of the IEEE CIS ECTC Task Force on Artificial Immune Systems (2018- ). He has been a member of the organizational team of more than ten academic conferences, in various functions, such as program chair, symposium chair and publicity chair.
Title: Evolutionary Multiparty Multiobjective Optimization
Abstract: Some real-world optimization problems involve multiple decision makers holding different positions, each of whom has multiple conflicting objectives. These problems are defined as multiparty multiobjective optimization problems (MPMOPs). First, this talk will be focused on a special class of MPMOPs, which have common Pareto optimal solutions. The basic algorithm and the benchmark about MOMOPs having common Pareto optimal solutions are introduced. Second, the real MPMOPs related to distance minimization problems (DMPs) will be introduced. DMPs are a type of multiobjective optimization problems, which demand the nearest position to a set of predefined fixed positions. But the multiparty DMPs mean that there are multiple decision makers, and each decision maker corresponds to one DMP. Two algorithms are tested on the benchmarks, including OptMPNDS and OptMPNDS2. OptMPNDS based on NSGAII changes the nondominated sorting to rank the solutions since multiple parties are involved. OptMPNDS2 further enhances the nondominated sorting to deal with the dominance relation more precisely.