Particle Swarm Optimization and Inverse Problems

摘要

In this paper we present a powerful set of Particle Swarm optimizers for inverse modeling. Their design is based on the interpretation of the swarm dynamics as a stochastic damped mass-spring system. All the PSO optimizers have very different exploitation and exploration capabilities. Their convergence can be related to the stability of their first and second order moments of the particle trajectories. Based on these results we present their corresponding cloud algorithms where each particle in the swarm has different inertia (damping) and acceleration (rigidity) constants. These algorithms show a very good balance between exploration and exploitation and their use avoids the tuning of the PSO parameters. These algorithms have been successfully applied to environmental geophysics and petroleum reservoir engineering where the combined use of model reduction techniques allow posterior sampling in high dimensional spaces.