A surrogate-assisted hybrid optimization algorithm enhanced by opposition-based learning and its variants

doi:10.1088/1742-6596/1948/1/012034

	A surrogate-assisted hybrid optimization algorithm enhanced by opposition-based learning and its variants
	Ling,Zhaoqi 1,2
刊名	Journal of Physics: Conference Series
	2021-06-01
卷号	1948 期号:1
ISSN号	1742-6588
DOI	10.1088/1742-6596/1948/1/012034
英文摘要	Abstract Surrogate-Assisted Evolutionary Algorithms (SAEAs) are effective approaches to solve computationally expensive optimization problems by remarkably reducing real fitness evaluations. In this work, we proposed a surrogate-assisted hybrid optimization algorithm via combining a famous hierarchical SAEA Framework ESAO and a hybrid teaching-learning based optimization (TLBO) algorithm TLBO-SM. In addition, opposition-based learning (OBL) and its variants are used to enhance the global search ability. Experimental results on benchmark problems show that our method can outperform state-of-the-art SAEAs on most benchmark problems with the significant enhancement made by a recently proposed OBL variant.
语种	英语
出版者	IOP Publishing
WOS记录号	IOP:1742-6588-1948-1-012034
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/58526]
专题	中国科学院数学与系统科学研究院
作者单位	1.University of the Chinese Academy of Sciences, China 2.Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
推荐引用方式 GB/T 7714	Ling,Zhaoqi. A surrogate-assisted hybrid optimization algorithm enhanced by opposition-based learning and its variants[J]. Journal of Physics: Conference Series,2021,1948(1).
APA	Ling,Zhaoqi.(2021).A surrogate-assisted hybrid optimization algorithm enhanced by opposition-based learning and its variants.Journal of Physics: Conference Series,1948(1).
MLA	Ling,Zhaoqi."A surrogate-assisted hybrid optimization algorithm enhanced by opposition-based learning and its variants".Journal of Physics: Conference Series 1948.1(2021).