Adaptive wireless network multi-objective optimization algorithm based on image synthesis

Jianwei Zhang,Xueya Zhang

doi:10.1186/s13640-018-0289-3

Abstract

Multi-objective optimization problems can be divided into continuous multi-objective optimization problems and discrete multi-objective optimization problems, and discrete multi-objective optimization is not universal. In practical applications, there are many discrete multi-objective optimization problems. The solution of different problems needs to design different evolutionary multi-objective algorithms according to their specific conditions. The threshold in the traditional multi-objective optimization of wireless network is a preset constant. It has relatively poor performance in the synthesis of images with different noise levels. An adaptive wireless network multi-objective optimization algorithm based on image synthesis is proposed. Based on the maximum inter class variance and maximum peak signal to noise ratio (SNR), an adaptive wireless network multi-objective optimization algorithm is established. The accuracy and noise immunity of image synthesis are also considered. In order to avoid the effect of threshold increase on algorithm efficiency, multi-objective optimization algorithm is introduced into the algorithm. Experiments show that the method proposed in this paper is accurate and robust and has good universality for the synthesis of different noise images.

Highlights

Image synthesis plays an important role in the field of computer vision and the pattern recognition [1]
N Þ, while shown in the complexity of the ptraffiffiditional multi-objective optimization algorithm is 3L. This indicates that the multi-objective optimization algorithm of the adaptive wireless network put forward in this paper can take the accuracy and the noise immunity of the image synthesis into consideration at the same time, the complexity of the algorithm is increased greatly, which has seriously affected the efficiency of the algorithm
4 Result and discussion: experimental testing and analysis In order to test the performance of the synthesis algorithm put forward in this paper, the following two groups of experiments are carried out: (1) test of the effectiveness of the adaptive wireless network multi-objective optimization algorithm, which is compared to the three-dimensional Otsu method in the Threshold value Threshold value Q Between-class Regional consistency variance (Otsu)

Summary

Introduction

Image synthesis plays an important role in the field of computer vision and the pattern recognition [1]. This indicates that the multi-objective optimization algorithm of the adaptive wireless network put forward in this paper can take the accuracy and the noise immunity of the image synthesis into consideration at the same time, the complexity of the algorithm is increased greatly, which has seriously affected the efficiency of the algorithm.

Results

Conclusion