报告人：澳大利亚新南威尔士大学 Ngai Ming Kwok 博士
题 目：Metaheuristics: Particle Swarm Optimizer
Optimization algorithms are a branch of applied mathematics. Almost every problem in engineering and science can be reduced to an optimization or search problem. Meta-heuristic algorithms are intelligent self-learning algorithms in which the particle swarm optimizer is based on the evolution of populations or groups of individuals (called particles) to search for optimal solutions. Since the algorithm update is a discrete-time process, the algorithm can be analyzed and improved from the perspective of the difference equation. This presentation reports the development of the second-order oscillatory and first-order difference particle swarm algorithms that are based on the first-order and second-order difference equations. Higher efficiency and effectiveness algorithms are then derived.
Dr Ngai Ming Kwok received the PhD from the University of Technology Sydney, Australia, in 2007. He was a research fellow at the University of Technology Sydney and Western Sydney University. He is now a lecturer and PhD supervisor at the University of New South Wales. Dr Kwok was a member of the Institute of Engineering and Technology and Chartered Engineer (1996-2015), and Member of the Institute of Electrical and Electronics Engineers (2010-2015). His research interests include intelligent computation, image processing, nonlinear modelling, and intelligent control. He had published in high-impact journals and international conferences with more than 170 papers and received more than 2500 citations.