讲座题目：Use of Traditional Optimization Methods in Multiobjective Evolutionary Computation

时间：2月20日周一下午4:10

地点：海韵校区行政楼C-505

讲座人：香港城市大学Qingfu Zhang教授，IEEE院士

讲座摘要：Multiobjective Evolutionary Computation has been a major research topic in the field of evolutionary computation for many years. It has been generally accepted that combination of evolutionary algorithms and traditional optimization methods should be a next generation multiobjective optimization solver. Decomposition methods have been well used and studied in traditional multiobjective optimization. It is well known that the Pareto optimal solution set of a continuous multiobjective problem often exhibits some regularity. In this talk, I will describe two multiobjective evolutionary algorithms: MOEA/D and RM-MEDA. Both of them borrow ideas from traditional optimization. MOEA/D decomposes a multiobjective problem into a number of subtasks, and then solves them in a collaborative manner.  MOEA/D provides a very natural bridge between multiobjective evolutionary algorithms and traditional decomposition methods. It has been a commonly used evolutionary algorithmic framework in recent years.  RM-MEDA makes use of the regularity property to model the distribution of Pareto optimal solutions in the search space, and then generates new solutions from the model thus built.  Machine learning techniques can be readily used in RM-MEDA. I will explain the basic ideas behind these two algorithms and some recent developments.   I will also outline some possible research issues in multiobjective evolutionary computation. 

报告人简介：Qingfu Zhang is a Professor at the Department of Computer Science, City University of Hong Kong and Essex University. His main research interests include evolutionary computation, optimization, neural networks, data analysis, and their applications.  He is currently leading the Metaheuristic Optimization Research (MOP) Group in City University of Hong Kong.  Professor Zhang is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the IEEE Transactions Cybernetics.  MOEA/D, a multiobjective optimization algorithm developed in his group, won the Unconstrained Multiobjective Optimization Algorithm Competition at the Congress of Evolutionary Computation 2009, and was awarded the 2010 IEEE Transactions on Evolutionary Computation Outstanding Paper Award. He is on the list of the Thomson Reuters 2016 highly cited researchers in computer science.  He is an IEEE fellow.