潘正祥来我所做学术报告

 Jeng-Shyang Pan received the B. S. degree in Electronic Engineering from the National Taiwan University of Science and Technology in 1986, the M. S. degree in Communication Engineering from the National Chiao Tung University, Taiwan in 1988, and the Ph.D. degree in Electrical Engineering from the University of Edinburgh, U.K. in 1996. Currently, he is the Doctoral Advisor in the Harbin Institute of Technology. He has published more than 500 papers in which 200 papers are indexed by SCI. He is the IET Fellow, UK and has been the Vice Chair of IEEE Tainan Section. He was Awarded Gold Prize in the International Micro Mechanisms Contest held in Tokyo, Japan in 2010. He was also awarded Gold Medal in the Pittsburgh Invention & New Product Exposition (INPEX) in 2010, Gold Medal in the International Exhibition of Geneva Inventions in 2011 and Gold Medal of the IENA, International “Ideas – Inventions – New products“, Nuremberg, Germany. He was offered Thousand Talents Program in China. He is on the editorial board of Journal of Information Hiding and Multimedia Signal Processing and Chinese Journal of Electronics. His current research interests include soft computing, robot vision and big data mining.  

Topic: Overview of Bio-Inspired Computing

Bio-Inspired Computing is based on collective behavior of self-organized systems. Typical Bio-Inspired schemes include particle swarm optimization (PSO), ant colony system (ACS), stochastic diffusion search (SDS), bacteria foraging (BF), the bees algorithm etc. Besides the applications to conventional optimization problems, Bio-Inspired Computing can be used in controlling robots and unmanned vehicles, predicting social behaviors, enhancing the telecommunication and computer networks, etc. Indeed, the use of Bio-Inspired Computing can be applied to a variety of fields in engineering and social sciences.  This talk reviews some popular algorithms in the field of Bio-Inspired Computing for problems of optimization. The detail overview of Particle Swarm Optimization and Cat Swarm Optimization, Ant Colony System (ACS), Monkey King Algorithm and  Artificial Bee Colony  are given.