基于改进型蛇算法的RFID网络规划部署

Enhanced Snake Optimizer Based RFID Network Planning

针对无线射频识别(RFID)网络规划的优化部署问题,提出一种基于Circle映射的嵌入正弦余弦算法和自适应阈值的改进型蛇算法(ESO)。在种群初始化阶段利用Circle混沌映射的均匀性和遍历性等特点,在局部搜索阶段和开发阶段分别引入正弦余弦算法(SCA)和自适应阈值等算法机制,弥补了蛇算法初始化过程不够均匀、容易陷入局部最优和收敛速度慢等缺点。在满足100%标签覆盖率、减少阅读器之间的碰撞干扰、实现阅读器的负载均衡,以及降低总的发射功率这4个目标的基础上,求解阅读器最佳的部署位置,将所提算法与粒子群算法(PSO)、灰狼算法(GWO)、樽海鞘算法(SSA)进行了对比分析。实验结果表明,改进型蛇算法在对RFID网络进行优化部署时寻优能力更强,对RFID网络部署的综合性能提升明显,在相同的实验条件下,ESO的最佳适应度值比PSO提高了28.1%,比GWO提高了17.7%,比SSA提高了22.9%,可以更有效地得出最优的RFID网络规划部署方案。

Aiming at the optimal deployment of radio frequency identification(RFID)network planning,an enhanced snake optimizer based on the embedded sine cosine algorithm(SCA)and adaptive threshold is proposed.In the population initialization stage,taking advantage of the uniformity and ergodicity of the Circle chaotic map,the algorithm mechanisms such as sine cosine algorithm and adaptive threshold are introduced in the local search stage and the development stage,respectively,to get rid of the disadvantages of the snake optimizer such as uneven initialization process,easy to fall into local optimization and slow convergence speed.On the basis of meeting the four objectives of 100%label coverage,reducing the collision interference between readers and writers,achieving the load balance of readers and writers,and reducing the total transmission power,the optimal deployment location of readers is solved.Enhanced snake optimizer(ESO)is compared with particle swarm optimization(PSO),grey wolf optimizer(GWO),and salp swarm algorithm(SSA).Experimental results show that enhanced snake optimizer has a stronger ability to optimize the deployment of RFID network,and its overall performance is significantly improved.Under the same experimental conditions,the optimal fitness value of ESO is 28.1%higher than PSO,17.7%higher than GWO,and 22.9%higher than SSA,which can more effectively obtain the optimal RFID network planning and deployment scheme.