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

文献类型：期刊论文

作者Ding, Shuxin1,6; Zhang, Tao1,5; Chen, Chen4; Lv, Yisheng3; Xin, Bin4; Yuan, Zhiming1,5; Wang, Rongsheng7; Pardalos, Panos M.2
刊名INFORMATION SCIENCES
出版日期2023-10-01
卷号645页码:22
ISSN号0020-0255
关键词Wireless sensor networks Stochastic area coverage Conditional value-at-risk Co-evolutionary particle swarm optimization Adaptive perturbation Evolutionary multitasking
DOI10.1016/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.
WOS关键词DEPLOYMENT ; ALGORITHM ; NETWORKS ; MUTATION
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)
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, Signal & Commun Res Inst, Beijing 100081, Peoples R China
2.Univ Florida, Ctr Appl Optimizat, Dept Ind & Syst Engn, Gainesville, FL 32611 USA
3.Chinese Acad Sci, State Key Lab Multimodal Artificial Intelligence S, Inst Automat, Beijing 100190, Peoples R China
4.Beijing Inst Technol, Sch Automat, Natl Key Lab Autonomous Intelligent Unmanned Syst, Beijing 100081, Peoples R China
5.Natl Engn Res Ctr Syst Technol High Speed Railway, Traffic Management Lab High Speed Railway, Beijing 100081, Peoples R China
6.China Acad Railway Sci Corp Ltd, Ctr Natl Railway Intelligent Transportat Syst Eng, Beijing 100081, Peoples R China
7.China Acad Railway Sci Corp Ltd, Sci & Tech Informat Res Inst, Beijing 100081, Peoples R China
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.

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来源：自动化研究所

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