报告题目：Recent Advances and Applications in Bacterial-inspired Optimization Algorithms
报告人：牛奔教授 深圳大学
时间：2020年12月18日（周五）上午 10:30
地点：管理学院第二会议室
【牛奔教授简介】
牛奔，男，博士(后)，教授，深圳大学、香港理工大学、澳门大学博士生导师。英国爱丁堡大学、美国亚利桑那州立大学、新西兰惠灵顿维多利亚大学访问学者，香港理工大学博士后、中国科学院博士后。 近五年来，获得多项荣誉，如2012年入选国家第二批“香江学者”、广东省高等学校“千百十人才”省级培养对象，2013年入选深圳市“高层次专业人才”，2014年度入选深圳“海外高层次人才孔雀计划”，2015入选广东省高校“优秀青年教师”，2016年入选“广东省珠江学者”，2018年入选教育部工业工程专业 “教指委委员”。近年来主持国家自然科学基金项目的6项，中国博士后科学基金项目的2项。长期从事管理、经济、计算机科学、生物、人工智能、创新创业等多学科交叉领域的科研工作，发表相关学术论文100余篇，其中SCI/SSCI论文55篇、EI收录88篇，出版专著7部，申请专利及软件著作权4个。获得过国际群体智能大会、国际智能计算大会、国际生物启发式计算与应用大会“最佳论文奖”、中国管理学年会优秀论文奖及其它各类学术奖项20项。
Abstract
Nowadays, most swarm intelligent optimization algorithms are inspired by the behavior of animals. By simulating the foraging behavior of E. coli in human intestines, Bacterial Foraging Algorithm (BFO) was proposed. However, due to the lack of communication among bacterial individuals and the adoption of a nested loop, the original BFO has several defects, including high memory consumption and low efficiency. Therefore, two BFO variants, including Bacterial Colony Optimization (BCO) and Coevolutionary Structure-Redesigned-Based Bacterial Foraging Optimization (CSRBFO), were developed to address the problems above. To be more specific, BCO considers chemotaxis strategy combined with a communication mechanism, while CSRBFO aims to fulfill tasks more effectively by using a single loop to replace the nested loop of BFO. In this talk, initially, the original BFO is described and the development and evolvement of BFO will be presented by using knowledge mapping. Then, two variants (BCO and CSRBFO) are presented in detail. Whereafter, some effective strategies for other improved bacteria-inspired optimization algorithms are introduced. Finally, some real-world applications and future work of bacteria-inspired optimization algorithms are discussed.
