基于改进型多目标樽海鞘群算法的RFID阅读器天线优化部署

随着射频识别(Radio Frequency Identification,RFID)技术的飞速发展,在各种特殊的环境下(如工厂、仓库、监狱等),对RFID阅读器天线优化部署的需求开始受到广泛关注.针对目前RFID阅读器天线部署中存在的部署难度大、约束条件多且不易找到最优解和Pareto前沿等问题,文中提出了一种基于改进型多目标樽海鞘群算法(Multi-obj ective Salp Swarm Algorithm,MSSA)的RFID阅读器天线优化部署方法.预先构建多目标RFID阅读器天线优化部署模型,设定优化目标;多目标樽海鞘群算法对RFID阅读器天线优化部署模型进行优化训练,引入分离算子以优化搜索能力,并通过迭代不断寻找满足条件的非支配解,构建满足条件的Pareto解集,其即为优化的结果.实验数据表明,MSSA算法求解时无需先验知识和设置加权系数,收敛速度快;在相同实验环境下,MSSA算法与带观察者机制的蝙蝠(BA-OM)算法、粒子群(PSO)算法、细菌觅食优化(MCBFO)算法相比,覆盖率分别提高了33％,28％,20％;与同类型的求Pareto解集的混合萤火虫(HMOFA)算法相比,MSSA算法的负载均衡提高了7.14％,经济效益提高了59.74％,阅读器干扰减少34.04％.     

基于改进型灰狼算法的RFID网络规划

随着物联网技术的飞速发展,射频识别(Radio Frequency Identification,RFID)系统因具有非接触、快速识别等优点而成为了解决物联网问题的首选方案。RFID网络规划问题要考虑多个目标,被证明是多目标优化的问题。群体智能(Swarm Intelligence,SI)算法在解决多目标优化问题方面得到了广泛的关注。文中提出了一种改进型灰狼算法(Improved Grey Wolf Optimizer,IGWO),利用高斯变异算子和惯性常量策略来实现RFID网络规划。通过建立优化模型,在满足标签100%覆盖率、部署更少的阅读器、避免信号干扰、消耗更少的功率4个目标的基础上,将所提算法与粒子群算法(Particle Swarm Optimization,PSO)、遗传算法(Genetic Algorithm,GA)、帝王蝶算法(Monarch Butterfly Algorithm,MMBO)进行了对比分析。实验结果表明,灰狼算法在RFID网络规划时表现更优异,在相同的实验环境下,相较于其他算法,IGWO的适应度值比GA提高了20.2%,比PSO提高了13.5%,比MMBO提高了9.66%;并且覆盖的标签数更多,可以更有效地求出最优化方案。     

Random mating mayfly algorithm for RFID network planning

In order to improve robustness and efficiency of the radio frequency identification (RFID) network, a random mating mayfly algorithm (RMMA) was proposed. Firstly, RMMA introduced the mechanism of random mating into the mayfly algorithm (MA), which improved the population diversity and enhanced the exploration ability of the algorithm in the early stage, and find a better solution to the RFID nework planning (RNP) problem. Secondly, in RNP, tags are usually placed near the boundaries of the working space, so the minimum boundary mutation strategy was proposed to make sure the mayflies which beyond the boundary can keep the original search direction, as to enhance the ability of searching near the boundary. Lastly, in order to measure the performance of RMMA, the algorithm is then benchmarked on three well -known classic test functions, and the results are verified by a comparative study with particle swarm optimization (PSO), grey wolf optimization (GWO), and MA. The results show that the RMMA algorithm is able to provide very competitive results compared to these well-known meta-heuristics, RMMA is also applied to solve RNP problems. The performance evaluation shows that RMMA achieves higher coverage than the other three algorithms. When the number of readers is the same, RMMA can obtain lower interference and get a better load balance in each instance compared with other algorithms. RMMA can also solve RNP problem stably and efficiently when the number and position of tags change over time.     

RFID network planning based on improved brain storm optimization algorithm

In order to improve the service quality of radio frequency identification (RFID) systems, multiple objectives should be comprehensively considered. An improved brain storm optimization algorithm GABSO, which incorporated adaptive learning operator and golden sine operator into the original brain storm optimization (BSO) algorithm, was proposed to solve the problem of RFID network planning (RNP). GABSO algorithm introduces learning operator and golden sine operator to achieve a balance between exploration and development. Based on GABSO algorithm, an optimization model is established to optimize the position of the reader. The GABSO algorithm was tested on the RFID model and dataset, and was compared with other methods. The GABSO algorithm's tag coverage was increased by 9.62% over the Cuckoo search (CS) algorithm, and 7.70% over BSO. The results show that the GABSO algorithm could be successfully applied to solve the problem of RNP.