关于Gary G. Yen教授和Jun Wang（王钧）教授学术报告的通知

学术报告1——State-of -the-art Evolutionary Algorithms for Many Objective Optimization
报告题目：State-of -the-art Evolutionary Algorithms for Many Objective Optimization
报告时间： 2014年9月17日14:30点
地 点： 大连海事大学 校史展览馆 阶梯报告厅
报告人： Gary G. Yen教授
报告人简况：
Gary G. Yen received the Ph.D. degree in electrical and computer engineering from the University of Notre Dame in 1992. He is currently a Regents Professor in the School of Electrical and Computer Engineering, Oklahoma State University. His research interest includes intelligent control, computational intelligence, evolutionary multiobjective optimization, conditional health monitoring, signal processing and their industrial/defense applications.
Gary was an associate editor of the IEEE Transactions on Neural Networks and IEEE Control Systems Magazine during 1994-1999, and of the IEEE Transactions on Control Systems Technology, IEEE Transactions on Systems, Man and Cybernetics and IFAC Journal on Automatica and Mechatronics during 2000-2010. He is currently serving as an associate editor for the IEEE Transactions on Evolutionary Computation and IEEE Transactions on Cybernetics. Gary served as Vice President for the Technical Activities, IEEE Computational Intelligence Society in 2004-2005 and is the founding editor-in-chief of the IEEE Computational Intelligence Magazine, 2006-2009. He was the President of the IEEE Computational Intelligence Society in 2010-2011 and is elected as a Distinguished Lecturer for the term 2012-2014. He received Regents Distinguished Research Award from OSU in 2009, 2011 Andrew P Sage Best Transactions Paper award from IEEE Systems, Man and Cybernetics Society, 2013 Meritorious Service award from IEEE Computational Intelligence Society and 2014 Lockheed Martin Aeronautics Excellence Teaching award. He is a Fellow of IEEE and IET.

报告摘要：
Evolutionary computation is the study of biologically motivated computational paradigms which exert novel ideas and inspiration from natural evolution and adaptation. The applications of population-based heuristics in solving multiobjective optimization problems have been receiving a growing attention. To search for a family of Pareto optimal solutions based on nature-inspiring problem solving paradigms, Evolutionary Multiobjective Optimization Algorithms have been successfully exploited to solve optimization problems in which the fitness measures and even constraints are uncertain and changed over time.
When encounter optimization problems with many objectives, nearly all designs performs poorly because of loss of selection pressure in fitness evaluation solely based upon Pareto optimality principle. This talk will survey recently published literature along this line of research- evolutionary algorithm for many-objective optimization and its real-world applications. Based on performance metrics ensemble, we will provide a comprehensive measure among all competitors and more importantly reveal insight pertaining to specific problem characteristics that the underlying evolutionary algorithm could perform the best. The experimental results confirm the finding from the No Free Lunch theorem: any algorithm’s elevated performance over one class of problems is exactly paid for in loss over another class.

学术报告2——Advances in Neurodynamic Optimization
报告题目：Advances in Neurodynamic Optimization
报告时间： 2014年9月17日15:30点
地 点： 大连海事大学 校史展览馆 阶梯报告厅
报告人： Jun Wang（王钧）教授
报告人简况：
Jun Wang is a Professor and the Director of the Computational Intelligence Laboratory in the Department of Mechanical and Automation Engineering at the Chinese University of Hong Kong. Prior to this position, he held various academic positions at Dalian University of Technology, Case Western Reserve University, and University of North Dakota. He also held various short-term visiting positions at USAF Armstrong Laboratory (1995), RIKEN Brain Science Institute (2001), Universite Catholique de Louvain (2001), Chinese Academy of Sciences (2002), Huazhong University of Science and Technology (2006–2007), and Shanghai Jiao Tong University (2008-2011) as a Changjiang Chair Professor. Since 2011, he is a National Thousand-Talent Chair Professor at Dalian University of Technology on a part-time basis. He received a B.S. degree in electrical engineering and an M.S. degree in systems engineering from Dalian University of Technology, Dalian, China. He received his Ph.D. degree in systems engineering from Case Western Reserve University, Cleveland, Ohio, USA. His current research interests include neural networks and their applications. He published over 170 journal papers, 15 book chapters, 11 edited books, and numerous conference papers in these areas. He is the Editor-in-Chief of the IEEE Transactions on Cybernetics since 2014 and a member of the editorial board of Neural Networks since 2012. He also served as an Associate Editor of the IEEE Transactions on Neural Networks (1999-2009), IEEE Transactions on Cybernetics and its predecessor (2003-2013), and IEEE Transactions on Systems, Man, and Cybernetics – Part C (2002–2005), as a member of the editorial advisory board of International Journal of Neural Systems (2006-2013), as a guest editor of special issues of European Journal of Operational Research (1996), International Journal of Neural Systems (2007), Neurocomputing (2008, 2014), and International Journal of Fuzzy Systems (2010, 2011). He was an organizer of several international conferences such as the General Chair of the 13th International Conference on Neural Information Processing (2006) and the 2008 IEEE World Congress on Computational Intelligence, and a Program Chair of the IEEE International Conference on Systems, Man, and Cybernetics (2012). He has been an IEEE Computational Intelligence Society Distinguished Lecturer (2010-2012, 2014-2016). In addition, he served as President of Asia Pacific Neural Network Assembly (APNNA) in 2006 and many organizations such as IEEE Fellow Committee (2011-2012); IEEE Computational Intelligence Society Awards Committee (2008, 2012, 2014), IEEE Systems, Man, and Cybernetics Society Board of Directors (2013-2015), He is an IEEE Fellow, IAPR Fellow, and a recipient of an IEEE Transactions on Neural Networks Outstanding Paper Award and APNNA Outstanding Achievement Award in 2011, Natural Science Awards from Shanghai Municipal Government (2009) and Ministry of Education of China (2011), and Neural Networks Pioneer Award from IEEE Computational Intelligence Society (2014), among others.

报告摘要：
Optimization is omnipresent in nature and society, and an important tool for problem-solving in science, engineering, and commerce. Optimization problems arise in a wide variety of applications such as the design, planning, control, operation, and management of engineering systems. In many applications (e.g., online pattern recognition and in-chip signal processing in mobile devices), real-time optimization is necessary or desirable. For such applications, conventional optimization techniques may not be competent due to stringent requirement on computational time. It is computationally challenging when optimization procedures have to be performed in real time to optimize the performance of dynamical systems.

The past three decades witnessed the birth and growth of neurodynamic optimization. Although a couple of circuit-based optimization methods were developed in earlier, it was perhaps Hopfield and Tank who spearheaded the neurodynamic optimization research in the context of neural computation with their seminal works in mid-1980's. Tank and Hopfield extended the continuous-time Hopfield network for linear programming. Kennedy and Chua developed a neural network for nonlinear programming. It is proven that the state of the neurodynamics is globally convergent and an equilibrium corresponding to an approximate optimal solution of the given optimization problems. Over the years, the neurodynamic optimization research has made significant progresses with numerous models with improved features for solving various optimization problems. Substantial improvements of neurodynamic optimization theory and models have been made in several dimensions.

In this talk, starting with the idea and motivation of neurodynamic optimization, we will review the historic review and present the state of the art of neurodynamic optimization with many models and selected applications. Theoretical results about the state stability, output convergence, and solution optimality of the neurodynamic optimization models will be given along with many illustrative examples and simulation results. Four classes of neurodynamic optimization model design methodologies (i.e., penalty methods, Lagrange methods, duality methods, and optimality methods) will be delineated with discussions of their characteristics. In addition, it will be shown that many real-time computational optimization problems in information processing, system control, and robotics (e.g., parallel data selection and sorting, robust pole assignment in linear feedback control systems, robust model predictive control for nonlinear systems, collision-free motion planning and control of kinematically redundant robot manipulators with or without torque optimization, and grasping force optimization of multi-fingered robotic hands) can be solved by means of neurodynamic optimization. Finally, prospective future research directions will be discussed.



　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　航海学院
　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　 2014.9