Improved Particle Swarm Optimization Algorithm for Solving Systems of Nonlinear Equations

The problem of nonlinear equation systems is of great significance for practical engineering. However, solving nonlinear equation systems efficiently and accurately has always been a difficult problem. Many scholars have proposed different solutions to this, but it is difficult to achieve accurate results at lower computational complexity. This paper aims to address the problem of traditional particle swarm optimization (PSO) algorithms being prone to getting stuck in local optima. From the perspective of learning factors, using trigonometric functions, an asymmetric nonlinear learning factor adjustment mechanism is developed to enhance the precision and convergence efficiency of PSO algorithms. The solution of nonlinear equation systems was implemented through computer programming, and the results were compared with other intelligent optimization algorithms such as genetic algorithms and improved PSO algorithms in other literature. The results showed that this paper achieved good results in solution accuracy and convergence speed, which is an effective algorithm improvement.