A hierarchical guidance strategy assisted fruit fly optimization algorithm with cooperative learning mechanism

doi:10.1016/j.eswa.2021.115342

	A hierarchical guidance strategy assisted fruit fly optimization algorithm with cooperative learning mechanism
	Zhao, Fuqing 1; Ding, Ruiqing 1; Wang, Ling 2; Cao, Jie 1; Tang, Jianxin 1
刊名	EXPERT SYSTEMS WITH APPLICATIONS
	2021-11-30
卷号	183
关键词	Fruit fly optimization algorithm Gaussian distribution estimation algorithm Hierarchical guidance Cooperative learning mechanism
ISSN号	0957-4174
DOI	10.1016/j.eswa.2021.115342
英文摘要	The fruit fly optimization algorithm (FOA) has drawn enormous attention from researchers and practitioners in the computation intelligence domain for the benefits of simple implementation mechanism and few parameters tuning requirement of FOA. However, FOA is hard to adapt directly to address complex continuous problems. A hierarchical guidance strategy assisted fruit fly optimization algorithm with cooperative learning mechanism (HGCLFOA) is proposed in this study. The population is divided into elitist and inferior subpopulations with the fitness of objective function. The population center is re-designed as an elitist subpopulation to maintain the diversity of the population. In the olfaction search stage, the hierarchical guidance strategy is introduced for local search according to the difference of solution qualities to assign inferior individuals to elitist individuals on different levels. Meanwhile, the inferior information is applied by the inferior solutions repairing strategy to deflect the prediction of the elitist subpopulation for preventing HGCLFOA from falling into the local optimum. In the vision search stage, a hybrid Gaussian distribution estimation strategy is adopted to extract the elitist information of previous generations to predict the distribution of potential elitist individuals in the next generation. The exploration and exploitation of the HGCLFOA are balanced by the cooperation between elitist subpopulation and inferior subpopulation. A random walk strategy is activated to assist the elitist solutions to jump out the local optimal. The parameters of the HGCLFOA are calibrated by DOE and ANOVA methods. The experimental results demonstrated that the HGCLFOA outperformed the classical FOA and state-of-arts variants of FOA.
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:000694989100007
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/148783]
专题	国际合作处（港澳台办）计算机与通信学院
作者单位	1.Lanzhou Univ Technol, Sch Comp & Commun Technol, Lanzhou 730050, Peoples R China; 2.Tsinghua Univ, Dept Automat, Beijing 10084, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Fuqing,Ding, Ruiqing,Wang, Ling,et al. A hierarchical guidance strategy assisted fruit fly optimization algorithm with cooperative learning mechanism[J]. EXPERT SYSTEMS WITH APPLICATIONS,2021,183.
APA	Zhao, Fuqing,Ding, Ruiqing,Wang, Ling,Cao, Jie,&Tang, Jianxin.(2021).A hierarchical guidance strategy assisted fruit fly optimization algorithm with cooperative learning mechanism.EXPERT SYSTEMS WITH APPLICATIONS,183.
MLA	Zhao, Fuqing,et al."A hierarchical guidance strategy assisted fruit fly optimization algorithm with cooperative learning mechanism".EXPERT SYSTEMS WITH APPLICATIONS 183(2021).