{"title":"有距离限制的最大加权目标覆盖问题的近似算法","authors":"Jianhong Jin, Yingli Ran, Zhao Zhang","doi":"10.1007/s10878-024-01166-2","DOIUrl":null,"url":null,"abstract":"<p>In this paper, we study approximation algorithms for the problem of <i>maximum weighted target cover with distance limitations</i> (MaxWTCDL). Given <i>n</i> targets <span>\\(T=\\left\\{ t_{1},t_{2},\\ldots ,t_{n}\\right\\} \\)</span> on the plane and <i>m</i> mobile sensors <span>\\(S=\\left\\{ s_{1},s_{2},\\ldots ,s_{m}\\right\\} \\)</span> randomly deployed on the plane, each target <span>\\(t_i\\)</span> has a weight <span>\\(w_{i}\\)</span> and the sensing radius of the mobile sensors is <span>\\(r_{s}\\)</span>, suppose there is a movement distance constraint <i>b</i> for each sensor and a total movement distance constraint <i>B</i>, where <span>\\(B>b\\)</span>, the goal of MaxWTCDL is to move the mobile sensors within the distance constraints <i>b</i> and <i>B</i> to maximize the weight of covered targets. We present two polynomial time approximation algorithms. One is greedy-based, achieving approximation ratio <span>\\(\\frac{1}{2v}\\)</span> in time <span>\\(O(mn^2)\\)</span>, where . The other is LP-based, achieving approximation ratio <span>\\(\\frac{1}{v}(1-e^{-1})\\)</span> in time <span>\\(T_{LP}\\)</span>, where <span>\\(T_{LP}\\)</span> is the time needed to solve the linear program.</p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"39 1","pages":""},"PeriodicalIF":0.9000,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Approximation algorithms for maximum weighted target cover problem with distance limitations\",\"authors\":\"Jianhong Jin, Yingli Ran, Zhao Zhang\",\"doi\":\"10.1007/s10878-024-01166-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>In this paper, we study approximation algorithms for the problem of <i>maximum weighted target cover with distance limitations</i> (MaxWTCDL). Given <i>n</i> targets <span>\\\\(T=\\\\left\\\\{ t_{1},t_{2},\\\\ldots ,t_{n}\\\\right\\\\} \\\\)</span> on the plane and <i>m</i> mobile sensors <span>\\\\(S=\\\\left\\\\{ s_{1},s_{2},\\\\ldots ,s_{m}\\\\right\\\\} \\\\)</span> randomly deployed on the plane, each target <span>\\\\(t_i\\\\)</span> has a weight <span>\\\\(w_{i}\\\\)</span> and the sensing radius of the mobile sensors is <span>\\\\(r_{s}\\\\)</span>, suppose there is a movement distance constraint <i>b</i> for each sensor and a total movement distance constraint <i>B</i>, where <span>\\\\(B>b\\\\)</span>, the goal of MaxWTCDL is to move the mobile sensors within the distance constraints <i>b</i> and <i>B</i> to maximize the weight of covered targets. We present two polynomial time approximation algorithms. One is greedy-based, achieving approximation ratio <span>\\\\(\\\\frac{1}{2v}\\\\)</span> in time <span>\\\\(O(mn^2)\\\\)</span>, where . The other is LP-based, achieving approximation ratio <span>\\\\(\\\\frac{1}{v}(1-e^{-1})\\\\)</span> in time <span>\\\\(T_{LP}\\\\)</span>, where <span>\\\\(T_{LP}\\\\)</span> is the time needed to solve the linear program.</p>\",\"PeriodicalId\":50231,\"journal\":{\"name\":\"Journal of Combinatorial Optimization\",\"volume\":\"39 1\",\"pages\":\"\"},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2024-04-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Combinatorial Optimization\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1007/s10878-024-01166-2\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Combinatorial Optimization","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s10878-024-01166-2","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
摘要
本文研究了有距离限制的最大加权目标覆盖(MaxWTCDL)问题的近似算法。给定平面上有 n 个目标(T=left/{ t_{1},t_{2},\ldots ,t_{n}\right\} ),平面上随机部署了 m 个移动传感器(S=left/{ s_{1},s_{2},\ldots ,s_{m}\right\} )、每个目标(t_i)都有一个权重(w_{i}\),移动传感器的感应半径为(r_{s}\),假设每个传感器都有一个移动距离约束 b 和一个总移动距离约束 B,其中(B>;b),MaxWTCDL 的目标就是在距离约束 b 和 B 的范围内移动移动传感器,使覆盖目标的权重最大化。我们提出了两种多项式时间近似算法。一种是基于贪婪的算法,可以在(O(mn^2)\)时间内达到近似率(\frac{1}{2v}\),其中 。另一种是基于 LP 的算法,可以在 \(T_{LP}\) 时间内实现近似率(\frac{1}{v}(1-e^{-1})),其中 \(T_{LP}\) 是求解线性规划所需的时间。
Approximation algorithms for maximum weighted target cover problem with distance limitations
In this paper, we study approximation algorithms for the problem of maximum weighted target cover with distance limitations (MaxWTCDL). Given n targets \(T=\left\{ t_{1},t_{2},\ldots ,t_{n}\right\} \) on the plane and m mobile sensors \(S=\left\{ s_{1},s_{2},\ldots ,s_{m}\right\} \) randomly deployed on the plane, each target \(t_i\) has a weight \(w_{i}\) and the sensing radius of the mobile sensors is \(r_{s}\), suppose there is a movement distance constraint b for each sensor and a total movement distance constraint B, where \(B>b\), the goal of MaxWTCDL is to move the mobile sensors within the distance constraints b and B to maximize the weight of covered targets. We present two polynomial time approximation algorithms. One is greedy-based, achieving approximation ratio \(\frac{1}{2v}\) in time \(O(mn^2)\), where . The other is LP-based, achieving approximation ratio \(\frac{1}{v}(1-e^{-1})\) in time \(T_{LP}\), where \(T_{LP}\) is the time needed to solve the linear program.
期刊介绍:
The objective of Journal of Combinatorial Optimization is to advance and promote the theory and applications of combinatorial optimization, which is an area of research at the intersection of applied mathematics, computer science, and operations research and which overlaps with many other areas such as computation complexity, computational biology, VLSI design, communication networks, and management science. It includes complexity analysis and algorithm design for combinatorial optimization problems, numerical experiments and problem discovery with applications in science and engineering.
The Journal of Combinatorial Optimization publishes refereed papers dealing with all theoretical, computational and applied aspects of combinatorial optimization. It also publishes reviews of appropriate books and special issues of journals.