报告题目: Multiobjective Evolutionary Computation based Decomposition
报 告 人：香港城市大学张青富教授
报告时间: 4月3日上午10:10
报告地点: 龙洞校区教学楼702
报告内容：
Many optimization problems in the real world, by nature, have multipleconflicting 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.
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