Research on distributed model forecasting parallel framework based on adaptive genetic algorithm

With the progress of the Internet information age and the rapid development of Internet technology, adaptive genetic algorithms have been applied in various industries to process data in various fields. However, the traditional adaptive genetic algorithm has problems such as slow convergence speed and easy falling into local optimum. Based on the above background, this paper mainly studies an improved strategy of adaptive genetic algorithm based on a density parameter optimization algorithm. In addition, the proposed improved strategy mainly uses the nearest neighbor collaborative filtering algorithm to improve the average distributed parallel quality of the algorithm, and optimizes the distributed parallel mode and distributed parallel characteristics of the adaptive genetic algorithm respectively. Therefore, the improved strategy has the features of simplicity and high efficiency, fast convergence speed, and strong global search ability, having important research significance in the field of data analysis.