讲 座 人

孙超利

讲座人

职称、职务

教授

主持人

潘正祥

讲座类型

R自然科学

讲座对象

全校师生

举办时间

2018年1月12日8:00-10：00

□社会科学

举办地点

C1-206

讲

座

人

简

介

Chaoli Sun received her B.Sc. And M.Sc. Degrees in Computer Application Technology from Hohai University, Nanjing, Jiangsu, China, and Ph.D. in Mechanical Design and Theory from Taiyuan University of Science and Technology, Taiyuan,Shanxi, China, in 2011. She is a Professor in the Department of Computer Science and Technology, Taiyuan University of Science and Technology. She was a Research Fellow with the Department of Computer Science, University of Surrey, working on an EU grant on SWARM_ORGAN from September 2014 to September 2016. Her main research interests include swarm intelligence and swarm robotics, surrogate-assisted swarm optimization, large-scale swarm optimization, and optimization of complex mechanical systems.

She is a member of the Evolutionary Computation Technical Committee of the IEEE Computational Intelligence Society, an Associate Editor of Soft Computing(Springer) and an Editorial Board Member of Complex& Intelligence System Intelligence Society, an Associate Editor of Soft Computing. Dr. Sun has published more than 30 papers as the first author in international journals and conferences.

讲    座

主要内容

Meta-heuristic algorithms have been shown to be powerful in optimization of engineering problems. However, a large number of fitness evaluations are required before locating near at the global optimum, which limits the application of meta-heuristic algorithm in solving computational expensive problems. In this topic, a new fitness estimation strategy, which was proposed to approximate the fitness value of individuals based on the positional relationship between individuals, will be firstly introduced. Then a surrogate-assisted cooperative swarm optimization algorithm will be given subsequently for solving high-dimensional computationally expensive problems, in which a surrogate-assisted particle  swarm optimization(PSO) algorithm and a surrogate-assisted social learning-based PSO(SL-PSO) algorithm cooperatively search for the global optimum, where two algorithms share promising solutions evaluated by the real fitness function.