Analysis and optimization methods for complex information systems based on artificial intelligence

Because of its dynamic, nonlinear and multi-objective trade-off, complex information systems pose a severe challenge to traditional analysis and optimization methods. The main purpose of this article is to propose an analysis and optimization method of complex information system based on artificial intelligence (AI). Taking intelligent traffic signal control system as an example, this article uses deep learning (DL) technology to build a data-driven behavior prediction model to realize accurate prediction of traffic flow. Then, using the hybrid optimization framework of reinforcement learning (RL) and evolutionary algorithm, the signal timing optimization strategy is designed. Finally, the effectiveness of the proposed method is verified by experiments. The results show that the root mean square error of the hybrid model proposed in this article is reduced to 8.3 vehicles /5 minutes, which is significantly better than the traditional method. In the aspect of signal timing optimization, the average delay time is reduced by more than 20% and the traffic efficiency is improved by 15%. The research in this article provides a new idea for intelligent analysis and optimization of complex information systems, and lays a foundation for practical application.