TU Wien CAIML

The State of the Art of Collaborative Neurodynamic Optimization

CAIML Colloquium with Jun Wang.

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On June 10, 2024, CAIML Colloquium with Jun Wang from the City University of Hong Kong took place.

Slides

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Abstract

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. Despite the success, almost all existing neurodynamic approaches a few years ago worked well only for optimization problems with convex or generalized convex functions. Effective neurodynamic approaches to optimization problems with nonconvex functions and discrete variables are rarely available.

In this talk, the advances in neurodynamic optimization were presented. Specifically, In the proposed collaborative neurodynamic optimization framework, multiple neurodynamic optimization models with different initial states were employed for scattered searches. In addition, a meta-heuristic rule in swarm intelligence (such as PSO) was used to reposition neuronal searches upon their local convergence to escape local minima toward global optima. Problem formulations and experimental results were elaborated to substantiate the viability and efficacy of several specific paradigms in this framework for supervised/semi-supervised feature selection, supervised learning, vehicle-task assignment, model predictive control, energy load dispatching, and financial portfolio selection.

About the Speaker

Jun Wang is the Dean of the School of Data Science and a Chair Professor of Computational Intelligence in the Department of Computer 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, and Shanghai Jiao Tong University. 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.