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Optimal RFID networks planning using a hybrid evolutionary algorithm and swarm intelligence with multi-community population structure

机译:使用混合进化算法和具有多社区人口结构的群智能来优化RFID网络规划

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The problem of choosing the optimum locations and the associated parameters of readers in RFID communication systems is considered. All these choices must satisfy a set of objectives, such as tag coverage, load balance, economic efficiency, and interference in order to obtain accurate and reliable network planning. In this paper, a novel optimization algorithm, namely the multi-community GA-PSO, is proposed to solve the complicated RFID network planning problem of large-scale system. The main idea of the algorithm is to divide the single population of the canonical PSO into multi-swarm and use the genetic selection and mutation strategy to improve particle swarm dynamic rules. The simulation results show that the proposed algorithm obtains the superior solution for networking planning problem than canonical PSO does.
机译:考虑了在RFID通信系统中选择最佳位置和阅读器的相关参数的问题。所有这些选择都必须满足一组目标,例如标签覆盖范围,负载平衡,经济效率和干扰,以便获得准确而可靠的网络规划。为了解决大规模系统中复杂的RFID网络规划问题,提出了一种新颖的优化算法,即多社区GA-PSO。该算法的主要思想是将规范PSO的单个种群划分为多群,并使用遗传选择和变异策略来改进粒子群动态规则。仿真结果表明,与经典的PSO算法相比,该算法能更好地解决网络规划问题。

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