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墨尔本皇家理工大学 Xiaodong Li博士学术讲座预告

作者:DMIR    来自:    发表时间:2016-03-26    浏览量:793


报告时间 :20160329日(周二)上午930

报告地点:广工大学城校园工学一号馆216室(学术报告厅)

报告题目: Recent Advances in Multimodal Optimization using Niching Methods

报告人:墨尔本皇家理工大学 Xiaodong Li 教授

报告人简介:

Xiaodong Li received his B.Sc. degree from Xidian University, Xi'an, China, and Ph.D. degree in information science from University of Otago, Dunedin, New Zealand, respectively. Currently, he is an Associate Professor at the School of Computer Science and Information Technology, RMIT University, Melbourne, Australia. His research interests include evolutionary computation, neural networks, complex systems, multiobjective optimization, and swarm intelligence. He serves as an Associate Editor of the IEEE Transactions on Evolutionary Computation, the journal of Swarm Intelligence (Springer), and International Journal of Swarm Intelligence Research. He is a founding member and currently a Vice-chair of the following three IEEE CIS Task Forces: Swarm Intelligence, Large Scale Global Optimization, and Multimodal Optimization.  He was the General Chair of SEAL'08, a Program Co-Chair AI'09, and a Program Co-Chair for IEEE CEC’2012.  He is the recipient of 2013 SIGEVO Impact Award. For further information, please visit his website: http://goanna.cs.rmit.edu.au/~xiaodong/.


报告摘要:

    This talk provides an update on the recent development of multimodal optimization methods, also commonly referred to as niching methods, in the area of evolutionary computation. Population-based meta-heuristic algorithms such as Evolutionary Algorithms (EAs) in their original forms are usually designed for locating a single global solution. These algorithms typically converge to a single solution because of the global selection scheme used. Nevertheless, many real-world problems are “multimodal” by nature, i.e., multiple satisfactory solutions exist. It may be desirable to locate many such satisfactory solutions so that a decision maker can choose one that is most proper in his/her problem domain. A niching method can be incorporated into a standard EA to promote and maintain formation of multiple stable subpopulations within a single population, with an aim to locate multiple globally optimal or suboptimal solutions. Many niching methods have been developed in the past, including crowding, fitness sharing, restricted tournament selection, clearing, speciation, etc. In more recent times, niching methods have also been developed for other meta-heuristic algorithms such as Particle Swarm Optimization (PSO) and Differential Evolution (DE).

    Currently niching methods are experiencing a revival, attracting researchers from across a wide range of research fields.  This talk will first provide information on the origin of niching methods, motivation on why research on niching is important, and its practical relevance to real-world problem solving.  It will revisit some of the most classic niching methods, before showing some recent development on niching methods, harnessing the unique characteristics of PSO and DE. I will then go on to show how niching methods can be employed to enhance performance in dynamic optimization and multiobjective optimization.  Finally I will discuss the top entries from the IEEE CEC’2015 niching methods competition.

 

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