Adaptive Optimization of Radio Frequency Identification with Gauss Traversal and Harmony Search Network in Internet of Things

To handle the large amount of data and complexity in the radio frequency identification process of the Internet of things, and overcome the disadvantages of lower complexity and recognition accuracy in the traditional algorithms, an algorithm was proposed to achieve the adaptive optimization of radio frequency identification with Gauss traversal and harmony search network in internet of things. First, the harmony search algorithm was used to optimize the design of the network. In order to solve the problems of the low optimizing accuracy and high computational complexity, the ergodicity of Gaussian function was applied to introducing control parameter in the improvisation process of the algorithm, which improves the pertinence for different evolution periods. The theoretical analysis for parameter selection was presented as well. Secondly, the optimization model of the radio frequency identification for the Internet of things was studied, and the improved adaptive optimization objective was proposed to achieve the equilibrium optimization of the performance index. Finally, the proposed algorithm was compared with the RPSOAS, CDE and C-MC algorithm, showing that, in the RFID (radio frequency identification network) experiment with 1000 m1000 m region and the 100000 tags, the convergence rate is 7.215 6, increased by more than 29.6%.