Optimization and Performance Analysis of Particle Swarm Optimization Algorithm in Learning Behavior System

With the rapid development of intelligent control technology, artificial intelligence has become an indispensable part of people's life. The particle swarm optimization algorithm has incomparable advantages. It obtains the optimal solution through information exchange between groups. In order to strengthen the learning efficiency of college students and improve their abilities, this paper uses the particle swarm algorithm to calculate the learning behavior of college students, and designs and optimizes the analysis module, strategy module and model of the system. In this paper, a genetic optimization model based on particle swarm optimization algorithm is established, and it is designed and simulated in a computer environment. For the iterative method, performance analysis and parameter estimation are proposed, and the entire learning process can be completed efficiently by continuously adjusting its own internal parameters. And by means of experimental analysis and algorithm performance comparison, the ability of particle swarm optimization algorithm is obtained. The experimental results show that the effectiveness of the improved multi-objective optimization particle swarm optimization algorithm in the learning behavior analysis system reaches 93%, and it is the most effective algorithm among the four algorithms. Therefore, in this system, a variety of particle swarm optimization algorithms can be combined to make full use of the MOPSO algorithm to improve system performance.