IHPG Algorithm for Efficient Information Fusion in Multi-Sensor Network via Smoothing Parameter Optimization

This investigate proposed a innovative Improved Hybrid PSO-GA (IHPG) algorithm which it combined the advantages of the PSO algorithm and GA algorithm. The IHPG algorithm uses the velocity and position update rules of the PSO algorithm and the GA algorithm in selection, crossover and mutation thought. This study explores the quality monitoring experiment by three existing neural network approaches to data fusion in wireless sensor module measurements. There are ten sensors deployed in a sensing area, the digital conversion and weight adjustment of the collected data need to be done. This experiment result can improve the accuracy of the estimated data and reduce the randomness of computing by adjustment optimization of smoothing parameter. According to the experimental analysis, the IHPG is better than the single PSO and GA in comparison the various neural network learning model.