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首页> 外文期刊>Bulletin of the Polish Academy of Sciences. Technical Sciences >Minimizing sensor movement in target coverage problem: A hybrid approach using Voronoi partition and swarm intelligence
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Minimizing sensor movement in target coverage problem: A hybrid approach using Voronoi partition and swarm intelligence

机译:在目标覆盖范围内使传感器移动最小化:使用Voronoi分区和群智能的混合方法

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This paper addresses the major challenges that reside on target coverage problem, which is one among the two primary sub-problems of node deployment problem. In order to accomplish a cost-efficient target coverage, a Voronoi partition-based, velocity added artificial bee colony algorithm (V-VABC) is introduced. The V-VABC is an advancement over the traditional, target-based Voronoi greedy algorithm (TVgreedy). Moreover, the VABC component of V-VABC is a hybrid, heuristic search algorithm developed from the context of ABC and particle swarm optimization (PSO). The V-VABC is an attempt to solve the network, which has an equal number of both sensors and targets, which is a special case of TCOV. Simulation results show that V-VABC performs better than TV-greedy and the classical and base algorithms of V-VABC such as ABC and PSO.
机译:本文解决了目标覆盖问题所面临的主要挑战,目标覆盖问题是节点部署问题的两个主要子问题之一。为了实现具有成本效益的目标覆盖范围,引入了基于Voronoi分区的速度增加的人工蜂群算法(V-VABC)。 V-VABC是对传统的基于目标的Voronoi贪婪算法(TVgreedy)的改进。此外,V-VABC的VABC组件是一种混合启发式搜索算法,是从ABC和粒子群优化(PSO)的上下文中开发的。 V-VABC是一种尝试解决网络的尝试,该网络具有相同数量的传感器和目标,这是TCOV的特例。仿真结果表明,V-VABC的性能优于电视贪婪算法,以及ABC和PSO等V-VABC的经典算法和基本算法。

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