Qingfu Zhang is a Chair Professor of Computational Intelligence with the Department of Computer Science, City University of Hong Kong. His is an IEEE fellow. His main research interests include evolutionary computation, optimization, neural networks, machine learning and their applications.
His multiobjective optimization evolutionary algorithm based on decomposition (MOEA/D) has been one of the most researched and used algorithms in the field of evolutionary computation and many application areas.
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 decade, 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.