报告题目：演化超多目标优化研究现状

报 告 人：Gary G. Yen, Regents Professor, IEEE Fellow, IET Fellow, Oklahoma State University

主 持 人：周爱民

报告时间：2018年5月17日 周四 10:00-11:00

报告地点：中北校区理科大楼A510

报告人简介：

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, IEEE Transactions on Emerging Topics on Computational Intellifgence 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 terms 2012-2014 and again 2016-2018.  He chaired 2006 IEEE World Congress on Computational Intelligence and again 2016 IEEE World Congress on Computational Intelligence, both held in Vancouver, Canada.  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.  Currently he serves as the chair of IEEE/CIS Fellow Committee. He is a Fellow of IEEE and IET.

报告摘要：

演化计算是一类受自然演化与自适应规则启发的计算模型。近年来，采用这种群体启发方法来求解多目标优化问题日益引起人们的关注。对于复杂的多目标优化问题，甚至是目标或约束函数包含不确定因素或发生动态变化，演化算法均能有效求解得到这些问题的Pareto最优解。然而当面临超多目标优化问题时，由于Pareto最优定义导致演化算法选择压力过小，这使得几乎所有的演化多目标优化算法搜索性能下降，给算法设计带来了巨大挑战。报告介绍并分析目前针对超多目标优化问题的演化算法设计，着重介绍在实际应用中人们所关注的三个问题及解决方案，即求解过程的可视化、算法性能评估及多目标决策。