A particle swarm optimization method applied to global optimization of inverse problem

Particle Swarm Optimization (PSO) is a global optimal algorithm based on swarm intelligence. PSO is more popular due to its easiness in implementation and fast convergence speed. However, PSO will be trapped into local optima while it is used to solve complex, multimodal inverse problems. To establish a proper balance between the exploration and exploitation searches, this paper introduces a dynamic and adaptive mechanism for the three basic parameters. To preserve the diversity of the swarm, a particular best particle, which takes part in a mutation operation, is introduced in the modified PSO. The numerical results on a case study show that the proposed PSO finds the best results as compared to other ones.