报告题目：Pareto Local Search and Decomposition
报 告 人：张青富
报告时间：2018年12月17日 周一 上午9:00-10:00
报告摘要：Local Search is a basic building block in modern heuristics for single objective optimization. However, most Pareto local search algorithms for multiobjective optimization is very costly. They cannot be easily used as a basic component in a multiobjective optimization problem solver. In this talk, I will report our recent work on decomposition based Pareto local search, which is much efficient than conventional Pareto local search. I expect that further research on this new Pareto local search will shed some new lights on design of multiobjective heuristics.
Prof. Qingfu Zhang is a Professor at the Department of Computer Science, City University of Hong Kong, 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 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 Cybernetics. He was awarded the 2010 IEEE Transactions on Evolutionary Computation Outstanding Paper Award. He is a 2016, 2017 and 2018 highly cited researcher in Computer Science (Clarivate Analytics) and an IEEE fellow. He is in the 1000 talents program and a Changjiang chair professor.