报告时间：2025年3月13日上午10:00

报告地点：腾讯会议号：971827947

报 告 人：周梦初教授

主办单位：计算机科学与技术学院

报告人简介：

周孟初教授，教育部长江学者讲座教授，澳门科技大学系统工程研究所教授，博士生导师。 1983年获得南京理工大学控制工程学士学位，1986年获得北京理工大学自动控制硕士学位，1990年获得伦斯勒理工学院计算机与系统工程博士学位。他于1990年加入新泽西理工学院，并自2013年起担任电气和计算机工程特聘教授。主要研究方向为Petri网、智能自动化、人工智能、云/边缘计算、物联网、大数据、网络服务和智能交通。他发表了1300多篇出版物，包括17本书，900多篇期刊论文（IEEE Transactions论文700多篇），31项专利和32个书籍章节。他是IEEE Press Book Series on Systems Science and Engineering的创始编辑，也是IEEE Internet of Things、IEEE Transactions on Intelligent Transportation Systems和IEEE Transactions on Systems, Man, and Cybernetics: Systems的副主编。2018-2022年任IEEE/CAA Journal of Automatica Sinica主编。他获得美国高级科学家洪堡研究奖，IEEE SMC Society的Franklin V. Taylor纪念奖和Norbert Wiener奖，新泽西州研究与发展委员会颁发的爱迪生专利奖，新泽西理工大学的卓越研究奖和奖章以及新泽西州研究与发展委员会的爱迪生专利奖的获得者。自2012年以来，他一直是高被引学者，并于2012年在Web of Science的全球工程领域排名第一。他目前的谷歌引用数超过77,200次，H-Index为141，2023年在Research.com评选的全球计算机科学领域知名学者排名中位列99。他是电气电子工程师学会（IEEE）、国际自动控制联合会（IFAC）、美国科学促进会（AAAS）、中国自动化学会（CAA）和美国国家发明家学会（NAI）的会士。

报告内容：

A Particle Swarm Optimizer (PSO) and mobile robot swarm are two widely studied subjects. Many applications emerge separately while the similarity between them is rarely explored. When a solution space is a certain region in reality, a robot swarm can replace a particle swarm to explore the optimal solution by performing PSO. In this way, a mobile robot swarm should be able to efficiently explore an area just like a particle swarm and uninterruptedly work even under the shortage of robots or in the case of unexpected failure of robots. Furthermore, the moving distances of robots are highly constrained because energy and time of robots can be costly. Inspired by such requirements, this presentation discusses a Moving-distance-minimized PSO for a mobile robot swarm to minimize the total moving distance of its robots while performing optimization and collaboration. The distances between the current robot positions and the particle ones in the next generation are utilized to derive paths for robots such that the total distance that all robots move is minimized, hence minimizing the energy and time for a robot swarm to locate the optima. Experimental results on optimizing 28 CEC2013 benchmark functions show the advantage of the proposed method over the standard PSO. By adopting it, the moving distance of robots can be reduced by more than 40% while offering the same optimization effects. The implication is enormous since all population-based optimization algorithms can be potentially benefited from such replacement of their individuals with mobile robots, thus leading to their moving-distance-minimized variants.