活动日期：2013-10-24
活动时间：15:00
活动地点：轮机学院会议室 ( 轮机楼302 )
内容：
应轮机工程学院邀请，IEEE fellow、美国俄克拉荷马州大学教授Gary G. Yen博士来访并做学术报告。
报告时间： 2013年10月24日15点
地点： 轮机学院会议室 ( 轮机楼302 )
报告题目： ADAPTIVE MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION BASED ON PARALLEL CELL COORDINATE SYSTEM
报告人： Gary G. Yen教授
报告人简况：
Gary G. Yen美国俄克拉荷马州大学（Oklahoma State University）教授，是IEEE和IET的会士（Fellow of IEEE and IET）。Gary G. Yen1992年从圣母大学（the University of Notre Dame）获得博士学位。 研究领域主要包括智能控制、计算智能、多目标优化、健康监测、信号处理和相关的工业/国防应用。
Gary G. Yen 担任过IEEE Transactions on Neural Networks 、IEEE Control Systems Magazine 、IEEE Transactions on Control Systems Technology、 IEEE Transactions on Systems, Man and Cybernetics、 IFAC Journal on Automatica and Mechatronics 等重要刊物的副编。现任IEEE Transactions on Evolutionary Computation 副编。2004-2005年，他是IEEE计算智能学会负责技术活动的副总裁；2006-2009年，他是IEEE Computational Intelligence Magazine的创刊主编。2010-2011年，他是IEEE计算智能学会总裁并当选为2012-2014年的卓越讲师。他还获得多项其它重要奖励。
附: 报告摘要和Gary G. Yen教授的简要情况：
ADAPTIVE MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION BASED ON PARALLEL CELL COORDINATE SYSTEM
Gary G. Yen, Ph.D., FIEEE, FIET
Oklahoma State University
School of Electrical and Computer Engineering
Managing convergence and diversity is essential in the design of Multiobjective Particle Swarm Optimization (MOPSO) in search for an accurate and well-distributed approximation of the true Pareto-optimal front. Largely due to its fast convergence, Particle Swarm Optimization incurs a rapid loss of diversity during the evolutionary process. Many mechanisms have been proposed in existing MOPSOs in term of leader selection, archive maintenance, and perturbation to tackle this deficiency. However, few MOPSOs are designed to dynamically adjust the balance in exploration and exploitation according to the feedback information detected from the evolutionary environment. In this paper, a novel method, named Parallel Cell Coordinate System (PCCS), is proposed to assess the evolutionary environment including density, rank, and diversity indicators based on the measurements of parallel cell distance, potential, and distribution entropy, respectively. Based on the PCCS, strategies proposed for selecting global best and personal best, maintaining archive, adjusting flight parameters, and perturbing stagnation are integrated into a self-adaptive MOPSO, abbreviated as pccsAMOPSO. The comparative experimental results show that the proposed pccsAMOPSO outperforms the other eight state-of-the-art competitors on ZDT and DTLZ test suites in terms of the chosen performance metrics. An additional experiment for density estimation in MOPSO illustrates that the performance of PCCS is superior to that of adaptive grid and crowding distance in terms of convergence and diversity.
Biography
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 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. 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, and 2013 Meritorious Service award from IEEE Computational Intelligence Society. He is a Fellow of IEEE and IET.
