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RFID Network Planning by ABC Algorithm Hybridized with Heuristic for Initial Number and Locations of Readers

机译:基于ABC算法和启发式混合的RFID网络规划的初始数量和位置。

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In the presented paper we show artificial bee colony (ABC) algorithm adjusted for the radio frequency identification (RFID) network planning problem and hybridized with heuristic for estimating the initial number and locations of RFID readers. The RFID network planning problem (RNP) belongs to the group of multi-objective NP-hard optimization problems hence it is an excellent candidate for swarm intelligence application. In many RNP implementations the number of readers represents the variable of an algorithm which often leads to uncontrolled oscillations and difficult convergence. In our approach, we employed heuristic with the goal of finding initial number and locations of readers for covering RFID network and then, when the number and approximate locations of the readers are estimated, we perform multi-objective RFID optimization with the ABC algorithm. We conducted empirical tests on six standard RFID sets of benchmark data which consist of clustered and random tag topologies. To test our approach, we performed a comparative analysis with other state-of-the-art algorithms whose performance was measured using the same test data. Our implementation obtained better results, especially considering the number of deployed readers.
机译:在本文中,我们展示了针对射频识别(RFID)网络计划问题进行了调整并与启发式算法相混合的人工蜂群(ABC)算法,用于估计RFID读取器的初始数量和位置。 RFID网络规划问题(RNP)属于多目标NP硬性优化问题,因此它是群体智能应用程序的极佳候选者。在许多RNP实现中,读取器的数量代表算法的变量,通常会导致不受控制的振荡和难以收敛。在我们的方法中,我们采用启发式方法,旨在找到覆盖RFID网络的读取器的初始数量和位置,然后在估计读取器的数量和大致位置时,使用ABC算法执行多目标RFID优化。我们对六种标准RFID基准数据集(包括集群和随机标签拓扑)进行了实证测试。为了测试我们的方法,我们与其他使用相同测试数据测量性能的最新算法进行了比较分析。我们的实施取得了更好的结果,特别是考虑到已部署阅读器的数量。

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