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李晓东教授学术报告会

2017-11-07

报告人:李晓东教授 (Professor at RMIT University, Melbourne, Australia)

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

报告地点:安徽大学理工D楼318

报告时间:2017年11月8日下午3:00—4:00

Abstract

Population or single solution search-based optimization algorithms(i.e., meta-heuristics) in their original forms are usually designed forlocating a single global solution. Representative examples include among othersevolutionary and swarm intelligence algorithms. These search algorithmstypically converge to a single solution because of the global selection schemeused. Nevertheless, many real-world problems are "multi-modal" bynature, i.e., multiple satisfactory solutions exist. It may be desirable tolocate many such satisfactory solutions, or even all of them, so that a decisionmaker can choose one that is most proper in his/her problem context.Numeroustechniques have been developed in the past for locating multiple optima (globaland/or local). These techniques are commonly referred to as "niching"methods, e.g., crowding, fitness sharing, derating, restricted tournamentselection, clearing, speciation, etc. In more recent times, niching methodshave also been developed for meta-heuristic algorithms such as Particle SwarmOptimization, Differential Evolution, and Evolution Strategies.

In this talk I will introduce niching methods, including itshistorical background, the motivation of employing niching in EAs. I willdescribe a few classic niching methods, such as the fitness sharing andcrowding methods, then provide a review on several new niching methods thathave been developed in meta-heuristics such as Particle Swarm Optimization andDifferential Evolution. Employing niching methods in real-world situationsstill face significant challenges, and this talk will discuss several suchdifficulties. In particular, niching in static and dynamic environments will bespecifically addressed.  Several examplesof applying niching methods to solving real-world optimization problems will beprovided as well.

 

Speaker Bio:

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 ofScience (Computer Science and Software Engineering), RMIT University,Melbourne, Australia. His research interests include evolutionary computation,neural networks, machine learning, complex systems, multiobjectiveoptimization, multimodal optimization (niching), and swarm intelligence. Heserves as an Associate Editor of the IEEE Transactions on EvolutionaryComputation, Swarm Intelligence (Springer), and International Journal of SwarmIntelligence Research. He is a founding member of IEEE CIS Task Force on SwarmIntelligence, a Vice-chair of IEEE CIS Task Force of Multi-Modal Optimization,and a former Chair of IEEE CIS Task Force on Large ScaleGlobal Optimization.  He was the GeneralChair of SEAL'08, a Program Co-Chair AI'09, a Program Co-Chair for IEEECEC’2012, a General Chair for ACALCI’2017 and AI’17. He is the recipient of2013 ACMSIGEVO Impact Award and 2017 IEEE CIS “IEEE Transactions onEvolutionary Computation Outstanding Paper Award”.

 

Related Publications:

Li, X., Epitropakis, M.G., Deb, K., and Engelbrecht, A. (2017),"Seeking Multiple Solutions: an Updated Survey on Niching Methods andTheir Applications", IEEETransactions on Evolutionary Computation,21(4):518 - 538, August 2017.

Islam, M.J., Li, X. and Deb, K., (2017), "Multimodal TrussStructure Design Using Bilevel and Niching Based Evolutionary Algorithms",in Proceedings of the 2017 Conference onGenetic and Evolutionary Computation Conference (GECCO), Berlin, Germany, ACM,pp.274-287, 2017.

Li, X., Engelbrecht, A. and Epitropakis, M.G. (2013),"Benchmark Functions for CEC'2013 Special Session and Competition onNiching Methods for Multimodal Function Optimization," Technical Report, EvolutionaryComputation and Machine Learning Group, RMIT University, Australia, 2013.

Li, X. (2011), "Developing Niching Algorithms in Particle SwarmOptimization", Handbook of SwarmIntelligence - Concepts, Principles and Applications, Series on Adaptation,Learning, and Optimization, Vol. 8, B.K. Panigrahi, Y.Shi, and M.-H. Lim(eds.), Springer, 2011, pp.67-88.