报告题目：MultiobjectiveEvolutionary Computation based on Decomposition
报告人：Prof. Qingfu Zhang
Many optimization problems in the real world, by nature, have multiple conflicting objectives. Unlike a single optimization problem, multiobjective optimization problem has a set of Pareto optimal solutions (Pareto front) which are often required by a decision maker. Evolutionary algorithms are able to generate an approximation to the Pareto front in a single run, and many traditional optimization methods have been also developed for dealing with multiple objectives. Combination of evolutionary algorithms and traditional optimization methods should be a next generation multiobjective optimization solver. Decomposition techniques have been well used and studied in traditional multiobjective optimization. Over the last several years, a lot of effort has been devoted to build efficient multiobjective evolutionary algorithms based on decomposition (MOEA/D). In this talk, I will describe main ideas and techniques and some recent development in MOEA/D. I will also discuss some possible research issues in multiobjective evolutionary computation.
Prof. Qingfu Zhang received the BSc in mathematics from Shanxi University, China in 1984, the MSc in applied mathematics and the PhD in information engineering from Xidian University, China, in 1991 and 1994, respectively. He is a Professor at the Department of Computer Science, City University of Hong Kong, Hong Kong, a Professor on leave from the School of Computer Science and Electronic Engineering, University of Essex, UK, and a Changjiang Visiting Chair Professor in Xidian University, China. He holds two patents and is the author of many research publications. 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.Prof. Zhang is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the IEEE Transactions on Systems, Man, and Cybernetics–Part B. He is also an Editorial Board Member of three other international journals.MOEA/D, a multobjevitve 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.