报告题目：Generative Model-Based Large-Scaled Dynamic Multiobjective Optimization
报告时间：2024年8月1日下午3:30–5:00
报告地点：太阳城集团校本部民主楼210
报告摘要：
Dynamic multi-objective optimization problems (DMOPs) are often scaled to large-scale scenarios in real-world applications, which inevitably must face the triple challenges of massive search space, dynamic environmental changes and multi-objective conflicts simultaneously. This talk will survey generative modeled-based approaches, specifically an adversarial autoencoder-based large-scale dynamic multi-objective evolutionary framework. It integrates deep generative modeling techniques and large-scale multi-objective evolutionary algorithms to solve large-scale DMOPs effectively and efficiently. Specifically, a deep generative network training architecture is proposed for high-dimensional decision variables in large-scale DMOPs. It can transfer a generative model trained on Pareto-optimal solutions in the current environment to a new environment using only the auxiliary information exhibited through the movement trajectories of historical Pareto-optimal solutions, resulting in the generation of quality initial populations for the new environment. Meanwhile, any large-scale multi-objective evolutionary algorithm can be integrated into the proposed framework without extensive modifications. Experimental results on a typical dynamic multi-objective test suite with problem settings from 30 to 1,000 dimensions demonstrate that the optimization performance of the proposed framework outperforms existing state-of-the-art designs. Especially in large-scale scenarios, the proposed framework is considered superior in terms of solution quality and computational efficiency.
专家简介：
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 theIEEE Transactions on Neural NetworksandIEEE Control Systems Magazineduring 1994-1999, and of theIEEE Transactions on Control Systems Technology,IEEE Transactions on Systems, Man and Cybernetics(Parts A and B) and IFAC Journal onAutomaticaandMechatronicsduring 2000-2010. He is currently serving as an associate editor for theIEEE Transactions on Evolutionary Computation,IEEE Transactions on Cybernetics,IEEE Transactions on Emerging Topics on Computational Intelligence, and most recentlyIEEE Transactions on Artificial 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 theIEEE Computational Intelligence Magazine, 2006-2009. He was elected to serve as the President of the IEEE Computational Intelligence Society in 2010-2011 and is elected as a Distinguished Lecturer for the term 2012-2014, 2016-2018, and 2021-2023. 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, IET and IAPR.
