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首页> 外文期刊>Industrial Informatics, IEEE Transactions on >Optimizing RFID Network Planning by Using a Particle Swarm Optimization Algorithm With Redundant Reader Elimination
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Optimizing RFID Network Planning by Using a Particle Swarm Optimization Algorithm With Redundant Reader Elimination

机译：使用消除冗余阅读器的粒子群优化算法优化RFID网络规划

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摘要

The rapid development of radio frequency identification (RFID) technology creates the challenge of optimal deployment of an RFID network. The RFID network planning (RNP) problem involves many constraints and objectives and has been proven to be NP-hard. The use of evolutionary computation (EC) and swarm intelligence (SI) for solving RNP has gained significant attention in the literature, but the algorithms proposed have seen difficulties in adjusting the number of readers deployed in the network. However, the number of deployed readers has an enormous impact on the network complexity and cost. In this paper, we develop a novel particle swarm optimization (PSO) algorithm with a tentative reader elimination (TRE) operator to deal with RNP. The TRE operator tentatively deletes readers during the search process of PSO and is able to recover the deleted readers after a few generations if the deletion lowers tag coverage. By using TRE, the proposed algorithm is capable of adaptively adjusting the number of readers used in order to improve the overall performance of RFID network. Moreover, a mutation operator is embedded into the algorithm to improve the success rate of TRE. In the experiment, six RNP benchmarks and a real-world RFID working scenario are tested and four algorithms are implemented and compared. Experimental results show that the proposed algorithm is capable of achieving higher coverage and using fewer readers than the other algorithms.
机译：射频识别（RFID）技术的快速发展带来了RFID网络最佳部署的挑战。 RFID网络规划（RNP）问题涉及许多约束和目标，并且已被证明是NP难的。进化计算（EC）和群智能（SI）求解RNP的使用已在文献中引起了广泛关注，但是提出的算法在调整网络中部署的阅读器数量方面遇到了困难。但是，已部署阅读器的数量对网络复杂性和成本产生了巨大影响。在本文中，我们开发了一种具有暂定阅读器消除（TRE）运算符的新型粒子群优化（PSO）算法来处理RNP。 TRE运算符在PSO的搜索过程中暂时删除了阅读器，如果删除降低了标签覆盖率，则能够在几代之后恢复被删除的阅读器。通过使用TRE，所提出的算法能够自适应地调整所使用的读取器的数量，以提高RFID网络的整体性能。此外，将变异算子嵌入到算法中以提高TRE的成功率。在实验中，测试了六个RNP基准和一个真实的RFID工作场景，并实施和比较了四个算法。实验结果表明，与其他算法相比，该算法能够实现更高的覆盖范围，使用更少的阅读器。

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