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。

Improved Grey Wolf Optimizer for RFID Network Planning

With the rapid development of Internet of things technology,radio frequency identification(RFID)system,with its advantages of non-contact and rapid identification,has become the first choice to solve the problem of Internet of things.RFID network planning should consider multiple objectives,which has been proved to be a multi-objective optimization problem.In this paper,an improved grey wolf optimizer is proposed,which uses Gauss mutation operator and inertia constant strategy to realize RFID network planning.Through the establishment of the optimization model,on the basis of satisfying the four objectives of 100%coverage of tags,deploying fewer readers,avoiding signal interference and consuming less power,this paper makes a comparative analysis with particle swarm optimization(PSO),genetic algorithm(GA)and monarch butterfly algorithm(MMBO).The experimental results show that grey wolf algorithm performs better in RFID network planning.In the same experimental environment,compared with other algorithms,the fitness value of IGWO is 20.2%higher than GA,13.5%higher than PSO,9.66%higher than MMBO,and the number of tags covered is more,so the optimization scheme can be found more effectively.