您當前位置: 发发彩票|官网登录  >  學術講座

講準字261號:超多目標進化優化研究最新進展

發布時間:2019-09-25|瀏覽次數:

講座報告主題:超多目標進化優化研究最新進展
專家姓名:Gary G. Yen
日期:2019-11-02 時間:10:00
地點:計算機學院208報告廳
主辦單位:計算機科學與通信工程學院  

主講簡介: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 (Parts A and B) 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 Cybernetics and IEEE Transactions on Emerging Topics on Computational Intelligence.  Gary served as Vice President for the Technical Activities, IEEE Computational Intelligence Society in 2004-2005 and was 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 and 2016-2018.  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.


主講內容:進化計算是研究受自然進化與自適應規則啟發的計算智能的一類分支,近年來,采用這種基于群體計算的啟發式范式求解多目標優化問題受到越來越多的關注。對于復雜的多目標優化問題,甚至是包含不確定因素或隨時間改變的動態目標或約束函數,多目標進化優化算法都能夠進行有效地求解发发彩票|官网登录,并得到這些問題的Pareto最優解发发彩票|官网登录。然而,在處理超多目標優化問題時发发彩票|官网登录,由于Pareto最優規則導致進化算法選擇壓力過小,現有的多目標優化算法往往表現不佳。因此发发彩票|官网登录,為應對維數災難所帶來的挑戰,進化算法的研究仍在持續發展。報告介紹并分析在實際應用中人們所關注的三個主要問題及解決方案发发彩票|官网登录,并著重介紹如何利用超多目標進化優化方法解決動態優化、約束優化、魯棒優化的最新進展以及它們在包括深度神經網絡自動設計上的應用。


歡迎師生參加!

发发彩票|官网登录