Collaborative Neurodynamic Optimization Approaches to Chiller Loading
Jun Wang
Department of Computer Science and School of Data Science,
City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong
The past four decades witnessed the birth and growth of neurodynamic optimization, which has emerged as a potentially powerful problem-solving tool for constrained optimization due to its inherent nature of biological plausibility and parallel and distributed information processing. In this talk, the advances in collaborative neurodynamic optimization will be delineated. Specifically, In the proposed collaborative neurodynamic optimization framework, multiple neurodynamic optimization models with different initial states are employed for scattered searches. In addition, a meta-heuristic rule in swarm intelligence (such as PSO) is used to reposition neuronal states upon their local convergence to escape local minima toward global optima. Experimental results will be elaborated to substantiate the efficacy of the methodology for centralized and distributed planning and control of chiller systems.
Biosketch
Jun Wang
is the Chair Professor in the Department of Computer Science and Dean of the School of Data Science at City University of Hong Kong. Prior to this position, he held various academic positions at Dalian University of Technology, Case Western Reserve University, University of North Dakota, and the Chinese University of Hong Kong. He also held various short-term visiting positions at USAF Armstrong Laboratory, RIKEN Brain Science Institute, Huazhong University of Science and Technology, Shanghai Jiao Tong University, and Swinburne University of Technology. He received a B.S. degree in electrical engineering and an M.S. degree from Dalian University of Technology and his Ph.D. degree from Case Western Reserve University. He was the Editor-in-Chief of the IEEE Transactions on Cybernetics. He is an IEEE Life Fellow, IAPR Fellow, and a foreign member of Academia Europaea. He is a recipient of the APNNA Outstanding Achievement Award, IEEE CIS Neural Networks Pioneer Award, CAAI Wu Wenjun AI Achievement Award, and IEEE SMCS Norbert Wiener Award, among other distinctions.