Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies

doi:10.1016/j.future.2020.04.008

科研成果详情

题名	Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies
作者	Chen, Hao 1; Heidari, Ali Asghar 2,3; Chen, Huiling 1; Wang, Mingjing 4; Pan, Zhifang5; Gandomi, Amir H.6
发表期刊	FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 影响因子和分区
语种	英语
原始文献类型	Article
关键词	Harris hawks optimization Optimization Nature-inspired algorithm Differential evolution Metaheuristic
其他关键词	PARTICLE SWARM OPTIMIZER ; ANT COLONY OPTIMIZATION ; GLOBAL OPTIMIZATION ; COMPUTATIONAL INTELLIGENCE ; FEATURE-SELECTION ; SEARCH ALGORITHM ; LOCAL SEARCH ; LEVY FLIGHTS ; DESIGN ; ADAPTATION
摘要	The first powerful variant of the Harris hawks optimization (HHO) is proposed in this work. HHO is a recently developed swarm-based stochastic algorithm that has previously shown excellent performance. In fact, the original HHO has features that can still be improved as it may experience convergence problems or may easily become trapped in local optima. To overcome these shortcomings of the original HHO, the first powerful variant of HHO integrates chaos strategy, topological multi-population strategy, and differential evolution (DE) strategy. For this, chaos mechanism is first introduced into the original algorithm to improve the exploitation propensities of HHO. The multi-population strategy with three mechanisms is embedded to augment the global search ability of the method. Finally, the DE mechanism is introduced into the HHO to enhance the quality of the solutions. Based on these well-regarded evolutionary mechanisms, we propose an enhanced DE-driven multi-population HHO (CMDHHO) algorithm. In this work, the proposed CMDHHO is compared with a range of other methods, including four original meta-heuristic algorithms, conventional HHO, twelve advanced algorithms based on IEEE CEC2017 benchmark functions, and IEEE CEC2011 real-world problems. Furthermore, the Friedman test and the non-parametric statistical Wilcoxon sign rank test are used to verify the significance of the results. The results of the experiments show that the three embedded mechanisms can effectively enhance the exploratory and exploitative traits of HHO. The time required for HHO to converge was substantially shortened. We suggest using the proposed CMDHHO as an effective tool to solve complex optimization problems. (C) 2020 Elsevier B.V. All rights reserved.
资助项目	Science and Technology Plan Project of Wenzhou, China [2018ZG012, 2018ZG016, ZG2017019]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [U1809209]; Graduate Scientific Research Foundation of Wenzhou University, China
出版者	ELSEVIER
出版地	AMSTERDAM
ISSN	0167-739X
EISSN	1872-7115
卷号	111 页码:175-198
DOI	10.1016/j.future.2020.04.008
WOS类目	Computer Science, Theory & Methods
WOS研究方向	Computer Science
WOS记录号	WOS:000541155100014
收录类别	SCIE ; EI ; SCOPUS
发表日期	2020-10
EI入藏号	20201908617286
EI主题词	Evolutionary algorithms
URL	查看原文
Scopus记录号	2-s2.0-85084181851
ESI热点论文	2021-05 ; 2021-07 ; 2021-11 ; 2022-01 ; 2022-03
ESI高被引论文	2021-03 ; 2021-05 ; 2021-07 ; 2021-09 ; 2021-11 ; 2022-01 ; 2022-03 ; 2022-07 ; 2022-09 ; 2022-11 ; 2023-01 ; 2023-03 ; 2023-05 ; 2023-07 ; 2023-09 ; 2023-11 ; 2024-01 ; 2024-03 ; 2024-05
引用统计	被引频次：208[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/19931
专题	附属第一医院
通讯作者	Chen, Huiling; Pan, Zhifang
作者单位	1.Wenzhou Univ, Dept Comp Sci & Artificial Intelligence, Wenzhou 325035, Peoples R China; 2.Univ Tehran, Coll Engn, Sch Surveying & Geospatial Engn, Tehran, Iran; 3.Natl Univ Singapore, Sch Comp, Dept Comp Sci, Singapore, Singapore; 4.Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam; 5.Wenzhou Med Univ, Affiliated Hosp 1, Wenzhou 325000, Peoples R China; 6.Univ Technol Sydney, Fac Engn & Informat Technol, Ultimo, NSW 2007, Australia
通讯作者单位	附属第一医院
推荐引用方式 GB/T 7714	Chen, Hao,Heidari, Ali Asghar,Chen, Huiling,et al. Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies[J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE,2020,111:175-198.
APA	Chen, Hao, Heidari, Ali Asghar, Chen, Huiling, Wang, Mingjing, Pan, Zhifang, & Gandomi, Amir H.. (2020). Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 111, 175-198.
MLA	Chen, Hao,et al."Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies".FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 111(2020):175-198.