Improved Genetic Algorithm to Optimize the Wi-Fi Indoor Positioning Based on Artificial Neural Network
Improved Genetic Algorithm to Optimize the Wi-Fi Indoor Positioning Based on Artificial Neural Network
复制标题
基于人工神经网络的改进遗传算法优化Wi-Fi室内定位
DOI：
10.1109/access.2020.2988322
复制
发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Wu, Chunlei
中科院分区：
文献类型：
--
作者：
Cui, Xuerong;Yang, Jin;Wu, Chunlei
为了更有效地利用Wi-Fi指纹数据进行目标定位，提出了一种改进的自适应遗传算法（IAGA）对BP（Back Propagation）神经网络进行优化，即IAGA-BP。该方法利用遗传算法的选择、交叉和变异操作来优化BP神经网络的权值和偏置。该算法一方面在保持最优策略的基础上对自适应遗传算法中的选择算子进行了改进。也就是说，每一代的群体将根据适应性从高到低排序，然后最高的20 &x0025;群体将直接遗传给下一代，而最差的20 &x0025;将被淘汰。剩余的80 &x0025;的人口将选择一个轮盘赌算法的基础上选择的每个个体的概率，以确保人口的体积不变。另一方面，对自适应遗传算法中的交叉概率和变异概率公式进行了改进。交叉率和变异率将根据个体适应性水平和种群当前的进化阶段进行调整，以保留上级个体和基因。仿真结果表明，与传统的Wi-Fi定位方法相比，本文提出的Wi-Fi定位方法具有更快的收敛速度和更好的定位精度（2.48米）。
In order to make more effective use of Wi-Fi fingerprint data to position an object, an improved adaptive genetic algorithm (IAGA) is proposed to optimize the BP (Back Propagation) neural network, namely, IAGA-BP. In this method, the selection, crossover and mutation operations of the genetic algorithm are used to optimize the weights and biases of the BP neural network. On the one hand, the proposed algorithm improves the selection operator in the adaptive genetic algorithm on the basis of preserving the optimal strategy. That is, the population of each generation will be sorted according to the adaptability from the highest to the lowest, then the highest 20 & x0025; of the population will be directly inherited to the next generation while the worst 20 & x0025; will be eliminated. The remaining 80 & x0025; of the population will be selected by a roulette algorithm based on the selection probability of each individual, as to ensure the population volume unchanging. On the other hand, the crossover and mutation probability formulas in the adaptive genetic algorithm are improved. The crossover and mutation rates will be adjusted to preserve superior individuals and genes according to the level of individual adaptability and the current evolution stage of the population. The simulation results show that compared with the traditional Wi-Fi positioning method, the proposed Wi-Fi positioning method has a faster convergence speed and better positioning accuracy of 2.48 meters.