An efficient particle swarm optimization with evolutionary multitasking for stochastic area coverage of heterogeneous sensors

doi:10.1016/j.ins.2023.119319

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	An efficient particle swarm optimization with evolutionary multitasking for stochastic area coverage of heterogeneous sensors
	Ding, Shuxin 2,3; Zhang, Tao 2,4; Chen, Chen 5; Lv, Yisheng 6; Xin, Bin 5; Yuan, Zhiming 2,4; Wang, Rongsheng 1; Pardalos, Panos M.7
刊名	INFORMATION SCIENCES
	2023-10-01
卷号	645 页码:22
关键词	Wireless sensor networks Stochastic area coverage Conditional value-at-risk Co-evolutionary particle swarm optimization Adaptive perturbation Evolutionary multitasking
ISSN号	0020-0255
DOI	10.1016/j.ins.2023.119319
通讯作者	Zhang, Tao(13701193534@139.com)
英文摘要	This paper investigates the stochastic area coverage problem of sensors with uncertain detection probability. The risk associated with uncertain parameters is managed using the conditional value-at-risk (CVaR) risk measure. The loss function is represented by the uncovered area coverage rate. We then formulate the minimum CVaR-based uncovered area coverage (CVaR-UAC) problem and provide some theoretical guarantees for the problem. Unlike previous research that treats area coverage as a single problem, we propose an efficient particle swarm optimization (PSO) with evolutionary multitasking to solve the stochastic area coverage problem along with multiple simplified problem forms. These simplified problems act as the auxiliary tasks for the original CVaR-UAC to enhance the evolutionary search. We have improved the proposed PSO algorithm from the framework of disturbance PSO and virtual force directed co-evolutionary particle swarm optimization, using a hybrid method in population initialization and an adaptive perturbation in individual updating. As a result, the exploration ability of the algorithm is significantly enhanced. The experiment results have demonstrated the effectiveness of the proposed algorithm compared with state-of-the-art algorithms in terms of solution quality.
资助项目	National Natural Science Foundation of China[62022015] ; National Natural Science Foundation of China[62273044] ; National Natural Science Foundation of China[62088101] ; National Natural Science Foundation of China[2022QNRC001] ; Young Elite Scientist Sponsorship Program by CAST[L2021G003] ; Foundation of China State Railway Group Company, Ltd.[2021YJ043] ; Foundation of CHINA ACADEMY OF RAILWAY SCIENCES CORPORATION LIMITED ; Paul and Heidi Brown Preeminent Professorship at ISE (University of Florida, USA) ; Humboldt Research Award (Germany) ; [62203468]
WOS关键词	DEPLOYMENT ; ALGORITHM ; NETWORKS ; MUTATION
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:001056509300001
资助机构	National Natural Science Foundation of China ; Young Elite Scientist Sponsorship Program by CAST ; Foundation of China State Railway Group Company, Ltd. ; Foundation of CHINA ACADEMY OF RAILWAY SCIENCES CORPORATION LIMITED ; Paul and Heidi Brown Preeminent Professorship at ISE (University of Florida, USA) ; Humboldt Research Award (Germany)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/54065]
专题	多模态人工智能系统全国重点实验室
通讯作者	Zhang, Tao
作者单位	1.China Acad Railway Sci Corp Ltd, Sci & Tech Informat Res Inst, Beijing 100081, Peoples R China 2.China Acad Railway Sci Corp Ltd, Signal & Commun Res Inst, Beijing 100081, Peoples R China 3.China Acad Railway Sci Corp Ltd, Ctr Natl Railway Intelligent Transportat Syst Eng, Beijing 100081, Peoples R China 4.Natl Engn Res Ctr Syst Technol High Speed Railway, Traffic Management Lab High Speed Railway, Beijing 100081, Peoples R China 5.Beijing Inst Technol, Sch Automat, Natl Key Lab Autonomous Intelligent Unmanned Syst, Beijing 100081, Peoples R China 6.Chinese Acad Sci, State Key Lab Multimodal Artificial Intelligence S, Inst Automat, Beijing 100190, Peoples R China 7.Univ Florida, Ctr Appl Optimizat, Dept Ind & Syst Engn, Gainesville, FL 32611 USA
推荐引用方式 GB/T 7714	Ding, Shuxin,Zhang, Tao,Chen, Chen,et al. An efficient particle swarm optimization with evolutionary multitasking for stochastic area coverage of heterogeneous sensors[J]. INFORMATION SCIENCES,2023,645:22.
APA	Ding, Shuxin.,Zhang, Tao.,Chen, Chen.,Lv, Yisheng.,Xin, Bin.,...&Pardalos, Panos M..(2023).An efficient particle swarm optimization with evolutionary multitasking for stochastic area coverage of heterogeneous sensors.INFORMATION SCIENCES,645,22.
MLA	Ding, Shuxin,et al."An efficient particle swarm optimization with evolutionary multitasking for stochastic area coverage of heterogeneous sensors".INFORMATION SCIENCES 645(2023):22.