2018年3月17日学术报告三则(邢立宁 王锐 伍国华,国防科技大学)
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报告题目1:学习型智能优化方法及其应用
 
报告日期及时间:2018年3月17号上午9:00
报告地点:      计算机学院B404
报告人:        邢立宁
报告人国籍:    中国
报告人单位:    国防科技大学
 
报告人简介: 
邢立宁,国防科技大学研究员,博士生导师,主要研究方向为智能优化、资源调度及任务规划等。博士论文被评为全国优秀博士学位论文,发表学术论文100余篇(SCI检索40余篇),所发论文共被引用800余次,七篇入选ESI引用前1%和10%论文。相关成果荣获湖南省自然科学二等奖、吴文俊人工智能科学技术二等奖和武警科学技术进步二等奖。入选教育部“新世纪优秀人才支持计划”;获得湖南省自然科学杰出青年基金项目。出版专著4部,获得13项国家发明专利授权,主持和参与国家自然科学基金等项目20余项。
 
报告摘要:
学习型智能优化方法采用智能优化模型和知识模型相结合的集成建模思路:智能优化模型按照邻域搜索策略对待优化问题的可行空间进行搜索;知识模型从前期优化过程中挖掘有用知识,然后采用知识来指导智能优化模型的后续优化过程。介绍了精英个体知识、构件知识、算子知识和参数知识等四种知识形式,构建了用于实现学习型智能优化方法的八类典型知识,可辅助学习型智能优化方法高效地求解复杂优化问题。结合成像卫星任务规划问题,重点介绍了学习型智能优化方法的典型应用。
 
邀请人: 王峰 副教授
 
 
 
报告题目2:Evolutionary many-objective optimization: methods and applications
 
报告日期及时间:2018年3月17号上午9:45
报告地点:      计算机学院B404
报告人:        王锐
报告人国籍:    中国
报告人单位:    国防科技大学
 
报告人简介: 
   王锐,国防科技大学,副教授,博士毕业于英国谢菲尔德大学,博士论文荣获The O.R. society (英国运筹学会)优秀博士论文奖。研究方向为智能多目标优化,机器学习,能源互联网系统优化等。 近五年发表包括IEEE TEVC,EJOR,INS在内的SCI/EI论文近50篇,研究成果曾荣获国防科技大学青年创新奖,吴文俊人工智能优秀青年奖,入选中国科协青年人才托举工程,获湖南省杰出青年基金资助,军队科技进步二等奖、湖南省自然科学二等奖各1项。目前主持国家自然科学基金青年和面上项目、湖南省杰出青年基金等多个课题。
 
报告摘要:
Many real-world problems have to be formulated as multi-objective problems where multiple objectives need to be optimized at the same time. Evolutionary algorithms have been demonstrated effective on solving two and three objective problems. However, the simultaneous optimization of many objectives (in excess of 3), in order to obtain a full and satisfactory set of tradeoff solutions to support a posteriori decision making, remains a challenging problem. In recent years, a number of algorithms have been proposed for solving many-objective problems. In this talk the preference inspired coevolutionary algorithms will be introduced. The PICEAs coevolve a family of decision-maker preferences together with a population of candidate solutions, and have shown promise for many-objective optimization. In addition, applications of PICEAs to energy internet management will be introduced.
 
邀请人: 王峰 副教授
 
 
 
报告题目3:Variable Reduction Strategy for Accelerating Evolutionary Optimization
 
报告日期及时间:2018年3月17号上午10:30
报告地点:      计算机学院B404
报告人:        伍国华
报告人国籍:    中国
报告人单位:    国防科技大学
 
报告人简介: 
    伍国华,国防科技大学系统工程学院讲师,分别于2008年和2014年在国防科技大学获得学士和博士学位,2012年至2014年为加拿大阿尔伯塔大学联合培养博士生。研究方向包括演化计算,机器学习,规划调度理论与应用。在IEEE TSMCA, Information Sciences, Computers & Operations Research等期刊发表论文40余篇,其中ESI前1%高被引论文1篇,目前担任期刊Information Science的客座编辑, Swarm and Evolutionary Computation Journal的副主编和International Journal of Bio-Inspired Computation的编委。是IEEE TEVC, IEEE TCYB, IEEE System Journal, Information Sciences 等20多个期刊的审稿人。
 
报告摘要:
We introduce a novel approach of variable reduction and integrate it into evolutionary algorithms in order to reduce the complexity of optimization problems. We develop reduction processes of variable reduction for derivative unconstrained optimization problems (DUOPs) and constrained optimization problems (COPs) with equality constraints and active inequality constraints. Variable reduction uses the problem domain knowledge implied when investigating optimal conditions existing in optimization problems. For DUOPs, equations involving derivatives are considered while for COPs, we discuss equations expressing the equality constraints. From the relationships formed in this way, we obtain relationships among the variables that have to be satisfied by optimal solutions. According to such relationships, we can utilize some variables (referred to as core variables) to express some other variables (referred to as reduced variables). We show that the essence of variable reduction is to produce a minimum collection of core variables and a maximum number of reduced variables based on a system of equations. We summarize some application-oriented situations of variable reduction and stress several important issues related to the further application and development of variable reduction. Essentially, variable reduction can reduce the number of variables and eliminate equality constraints, thus reducing the dimensionality of the solution space and improving the efficiency of evolutionary algorithms. The approach can be applied to unconstrained, constrained, continuous and discrete optimization problems only if there are explicit variable relationships to be satisfied in the optimal conditions. We test variable reduction on real-world and synthesized DUOPs and COPs. Experimental results and comparative studies point at the effectiveness of variable reduction.
 
邀请人: 王峰 副教授
 
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