基于粒子群算法的电磁场逆问题优化研究

Electromagnetic field inverse problems are widely applied in various fields, but due to their high nonlinearity and ill-posedness, they pose challenges to traditional solution methods. This paper focuses on this issue and conducts in-depth research on the application of particle swarm optimization algorithm in solving electromagnetic field inverse problems. By constructing an adaptive model, determining the search space and parameter range, initializing the particle swarm, using the fitness function to evaluate and update the particle states during iterations, and achieving the inversion of unknown parameters. Taking the two-dimensional static electric field inverse problem as an example for numerical experiments, the results show that the particle swarm optimization algorithm can effectively invert the charge distribution under the influence of noise interference. Compared with traditional methods, it has a faster convergence speed, can break through the limitation of local optimum, significantly improve the solution accuracy, and provide a new approach for solving electromagnetic field inverse problems. However, this algorithm has problems of slow convergence and being prone to local optimum in high-dimensional complex problems, and is sensitive to parameters. In the future, it can be improved by introducing adaptive inertia weight and hybridizing with other algorithms, and expanded to three-dimensional and multi-physics field coupling complex electromagnetic problems to promote the development of this field and provide better solutions for practical applications.