报告题目：STATE-OF-THE-ART EVOLUTIONARY ALGORITHMS FOR MANY OBJECTIVE OPTIMIZATION

报告人：Gary G. Yen 教授 美国俄克拉荷马州立大学

照片：

邀请人：高卫峰

报告时间：2019年7月26 10:00

报告地点：信远楼II205我院报告厅

报告人简介：

Gary G. Yen教授于1992年在美国圣母大学获电气与计算机工程专业的博士学位。目前为美国俄克拉荷马州立大学电气与计算机工程学院的Regents讲席教授(Regents Professor)。Yen教授是国际智能控制、计算智能、多目标优化等领域著名专家学者，在智能控制、多目标优化、健康监测及其工业/国防应用做出了突出的工作。Gary在1994-1999年期间担任IEEE Transactions on Neural Networks和IEEE Control Systems Magazine的副主编，2000-2010年期间是IEEE Transactions on Control Systems Technology、 IEEE Transactions on Systems, Man and Cybernetics、 IFAC Journal on Automatica and Mechatronics的副主编。他目前是IEEE Transactions on Evolutionary Computation 和IEEE Transactions on Cybernetics的副主编。Gary于2004-2005年担任IEEE计算智能学会技术活动副主席，2006-2009年担任IEEE Computational Intelligence Magazine的创始主编。他曾在2010-2011年担任IEEE计算智能学会主席，并被选为2012-2014年IEEE杰出讲师。他于2009年获得了俄克拉荷马州立大学的Regents杰出研究奖，2011年获得了IEEE系统、人与控制论学会的Andrew P Sage最佳论文奖，2013年获得了IEEE计算智能学会的卓越服务奖，2014年获得了洛克希德马丁航空公司卓越教学奖。他是IEEE Fellow和IET Fellow。

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 meta-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 metaphors, 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 perform poorly because of loss of selection pressure in fitness evaluation solely based upon Pareto optimality principle. During the last years, evolutionary algorithms have been adapted to address these challenges of curse of dimensionality. This talk will first survey recently published literature along this line of research- evolutionary algorithm for many-objective optimization and its real-world applications. Specific focus will be paid to multi-criteria decision making. A minimum Manhattan distance approach to multiple criteria decision making in many-objective optimization problems is considered. This procedure is equivalent to the knee selection described by a divide and conquer approach that involves iterations of pairwise comparisons. Given such a directive, knee-based evolutionary algorithms have also been exploited to address modern-day problems including automatic design of the deep neural networks.

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