报告题目：Seeking Multiple Solutions: Multi-Modal Optimization using Niching Methods

报告人：李晓东 教授  澳大利亚墨尔本皇家理工大学
照片：

邀请人：高卫峰 副教授
报告时间：2018年9月12日 16:00
报告地点：信远楼II206我院报告厅
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. He is a full professor at the School of Science (Computer Science and Software Engineering), RMIT University, Melbourne, Australia. His research interests include evolutionary computation, neural networks, machine learning, complex systems, multiobjective optimization, multimodal optimization (niching), and swarm intelligence. He serves as an Associate Editor of the IEEE Transactions on Evolutionary Computation, Swarm Intelligence (Springer), and International Journal of Swarm Intelligence Research. He is a founding member of IEEE CIS Task Force on Swarm Intelligence, a Vice-chair of IEEE CIS Task Force of Multi-Modal Optimization, and a former Chair of IEEE CIS Task Force on Large Scale Global Optimization.  He was the General Chair of SEAL'08, a Program Co-Chair AI'09, a Program Co-Chair for IEEE CEC’2012, a General Chair for ACALCI’2017 and AI’17. He is the recipient of 2013 ACM SIGEVO Impact Award and 2017 IEEE CIS “IEEE Transactions on Evolutionary Computation Outstanding Paper Award。
报告摘要：  Population or single solution search-based optimization algorithms (i.e., meta-heuristics) in their original forms are usually designed for locating a single global solution. Representative examples include among others evolutionary and swarm intelligence algorithms. These search algorithms typically converge to a single solution because of the global selection scheme used. Nevertheless, many real-world problems are "multi-modal" by nature, i.e., multiple satisfactory solutions exist. It may be desirable to locate many such satisfactory solutions, or even all of them, so that a decision maker can choose one that is most proper in his/her problem context.  Numerous techniques have been developed in the past for locating multiple optima (global and/or local). These techniques are commonly referred to as "niching" methods, e.g., crowding, fitness sharing, derating, restricted tournament selection, clearing, speciation, etc. In more recent times, niching methods have also been developed for meta-heuristic algorithms such as Particle Swarm Optimization (PSO) and Differential Evolution (DE). In this talk I will introduce niching methods, including its historical background, the motivation of employing niching in EAs, and the challenges in applying it to solving real-world problems. I will describe a few classic niching methods, such as the fitness sharing and crowding, as well as niching methods developed using new meta-heuristics such as PSO and DE.  Niching methods can be applied for effective handling of a wide range of problems including static and dynamic optimization, multiobjective optimization, clustering, feature selection, and machine learning. I will provide several such examples of solving real-world multimodal optimization problems.

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