基于知识的智能优化引导方法研究进展  

Research Progress on Intelligent Optimization Guidance Approaches Using Knowledge

作　　者：邢立宁[1] 陈英武[1] 

机构地区：[1]国防科学技术大学信息系统与管理学院符理系,长沙410073

出　　处：《自动化学报》2011年第11期1285-1289,共5页Acta Automatica Sinica

基　　金：国家自然科学基金(70971131,70601035,70801062)资助~~

摘　　要：为了提高智能优化方法的优化性能,国内外学者通过知识来加强对优化过程的引导.对基于知识的智能优化引导方法进行了综述:一方面通过传统人工智能手段来实现对智能优化方法的引导;另一方面通过特定知识模型来实现对智能优化方法的引导.从前期优化过程中挖掘有用知识,采用知识来引导后续优化过程,极大地提高了智能优化方法的优化性能.To improve the performance of intelligent optimization approaches, many researchers have used knowledge to strengthen the optimization process guidance. The intelligent optimization guidance using knowledge is reviewed in this work. The intelligent optimization guidance is normally executed via artificial intelligence approaches and special knowledge models. Also, some researchers have proposed algorithms which have a double layer evolution mechanism. These improved approaches can discover some knowledge from the previous iterations, then use the discoverd knowledge to guide the subsequent iterations.

关 键 词：人工智能 智能优化算法 引导 全局收敛 遗传算法 知识模型 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]