受tyc5997太阳集团邀请，香港城市大学张青富教授在公司举行学术报告。欢迎广大师生踊跃参加！
报告题目：MOEA/D: A Bridge between Traditional Optimization and Multiobjective Evolutionary Optimization
时间：6月8日上午9：30
地点：文昌校区逸夫楼邵407
主办单位：tyc5997太阳集团
报告内容：
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 MOEA/D and its recent progress. MOEA/D decomposes a multiobjective problem into a number of subtasks, and then solves them in a collaborative manner. It 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.
Qingfu Zhang is a Professor at the Department of Computer Science, City University of Hong Kong. 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 multi-objective optimization algorithm developed in his group, won the Unconstrained Multio-bjective 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 and 2017 highly cited researchers in computer science. He is a Fellow of IEEE. He is a Changjiang chair professor and was selected in 1000 talent program.
