学术报告(6月21日):Brain Storm Optimization Algorithms: From Model-driven and Data-driven Perspectives

发布者:潘春德发布时间:2018-06-15浏览次数:58

受中国矿业大学信息与控制工程学院邀请,陕西师范大学程适博士在我校举行学术报告。欢迎广大师生踊跃参加!

报告题目:Brain Storm Optimization Algorithms: From Model-driven and Data-driven Perspectives

间:621日上午1020-11:10

点:南湖信息与控制工程学院A311

主办单位:信息与控制工程学院

报告内容: For swarm intelligence algorithms, each individual in the swarm represents a solution in the search space, and it also can be seen as a data sample from the search space. Based on the analyses of these data, more effective algorithms and search strategies could be proposed. Brain storm optimization (BSO) algorithm is a new and promising swarm intelligence algorithm, which simulates the human brainstorming process. Through the convergent operation and divergent operation, individuals in BSO are grouped and diverged in the search space/objective space. In this talk, the history development, and the state-of-the-art of the BSO algorithm are reviewed. In addition, the convergent operation and divergent operation in the BSO algorithm are also discussed from the data analysis perspective. Every individual in the BSO algorithm is not only a solution to the problem to be optimized, but also a data point to reveal the landscape of the problem. Swarm intelligence and data mining techniques can be combined to produce benefits above and beyond what either method could achieve alone.

Shi Cheng (程适) received the Bachelor's degree in Mechanical and Electrical Engineering from Xiamen University, Xiamen, the Master's degree in Software Engineering from Beihang University (BUAA), Beijing, China, the Ph.D. degree in Electrical Engineering and Electronics from Liverpool University, Liverpool, United Kingdom, the Ph.D. degree in Electrical and Electronic Engineering from Xi’an Jiaotong-Liverpool University, Suzhou, China in 2005, 2008, and 2013, respectively. He is currently with School of Computer Science, Shaanxi Normal University, Xi’an, China. His current research interests include swarm intelligence, multiobjective optimization, and data mining techniques and their applications.