A Novel Surrogate-guided Jaya Algorithm for the Continuous Numerical Optimization Problems

doi:10.1109/CSCWD49262.2021.9437832

	A Novel Surrogate-guided Jaya Algorithm for the Continuous Numerical Optimization Problems
	Zhao, Fuqing1 ; Ma, Ru 1; Tang, Jianxin1 ; Zhang, Yi 2; Ma, Weimin 3
	2021
会议名称	24th IEEE International Conference on Computer Supported Cooperative Work in Design (IEEE CSCWD)
会议录名称	PROCEEDINGS OF THE 2021 IEEE 24TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN (CSCWD)
页码	96-101
会议日期	MAY 05-07, 2021
会议地点	Dalian, PEOPLES R CHINA
出版地	NEW YORK
出版者	IEEE
摘要	A new metaheuristic algorithm, named surrogate-guided algorithm(S-Jaya), is proposed to solve the single objective continuous optimization problems in this paper. A novel mutation strategy for the non-separable single objective continuous optimization problems is introduced to alter the search engine of the Jaya algorithm. The surrogate is embedded to accelerate the convergence of the population and avoid the proposed algorithm falling into the local optimal during the evolutionary process. The suggested S-Jaya algorithm to address the CEC 2017 benchmark problems is effective and validated. On the quality of solution and execution time, the experimental results reveal that the effectiveness of the S-Jaya algorithm is superior compare with the Jaya algorithm and its variants.
关键词	Jaya algorithm Mutation strategy Surrogate Radial basis function
DOI	10.1109/CSCWD49262.2021.9437832
收录类别	CPCI-S
语种	英语
WOS记录号	WOS:000716858200017
引用统计	被引频次[WOS]：0 [WOS记录] [WOS相关记录]
文献类型	会议论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/150139
专题	国际合作处（港澳台办）计算机与通信学院
通讯作者	Zhao, Fuqing
作者单位	1.Lanzhou Univ Technol, Sch Comp & Commun Technol, Lanzhou 730050, Peoples R China; 2.Xijin Univ, Sch Mech Engn, Xian 710123, Peoples R China; 3.Tongji Univ, Sch Econ & Management, Shanghai 200092, Peoples R China
第一作者单位	兰州理工大学
通讯作者单位	兰州理工大学
推荐引用方式 GB/T 7714	Zhao, Fuqing,Ma, Ru,Tang, Jianxin,et al. A Novel Surrogate-guided Jaya Algorithm for the Continuous Numerical Optimization Problems[C]. NEW YORK:IEEE,2021:96-101.