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SHENGXIANG YANG

Shengxiang Yang got his PhD degree in Control Theory and Control Engineering from Northeastern University, China in 1999. He is now a Professor of Computational Intelligence (CI) and Director of the Centre for Computational Intelligence (CCI), School of Computer Science and Informatics, De Montfort University, UK. He has worked extensively for many years in the areas of CI methods, including evolutionary computation and artificial neural networks, and their applications for real-world problems. He has over 300 publications with an H-index of 55 according to Google Scholar. His work has been supported by UK research councils, EU FP7 and Horizon 2020, Chinese Ministry of Education, and industry partners, with a total funding of over £2M. He serves as an Associate Editor or Editorial Board Member of several international journals, including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, Information Sciences, and Enterprise Information Systems, etc. He was the founding chair of the Task Force on Intelligent Network Systems (TF-INS, 2012-2017) and the chair of the Task Force on EC in Dynamic and Uncertain Environments (ECiDUEs, 2011-2017) of the IEEE Computational Intelligence Society (CIS). He has given over 20 keynote speeches and tutorials at international conferences.

Topic: Swarm Intelligence for Dynamic Optimization Problems

Swarm intelligence (SI) in biology represents the property that the collective behavior of a swarm of agents that interact locally with their environment causes coherent functional global patterns to emerge. SI algorithms are optimization algorithms inspired from the SI phenomena in biology, such as ant foraging and bird flocking, and have been applied in different fields. Most SI algorithms have been developed to address stationary problems. However, many real-world problems are dynamic optimization problems (DOPs) that are subject to changes over time. DOPs have attracted a growing interest from the SI community in recent years due to the importance in the real-world applications of SI algorithms. This talk will first briefly introduce the concepts of SI and DOPs, review the enhancement strategies integrated into SI algorithms to address DOPs, and then describe several detailed case studies on SI methods for DOPs. Finally, some conclusions will be made and the future work on SI for DOPs will be briefly discussed.