WINDMI is a low dimensional plasma physics-based model of the coupled magnetosphere-ionosphere system. The nonlinear system of ordinary differential equations describes the energy balance between the basic nightside components of the system using the solar wind driving voltage as input. Of the eight dynamical variables determined by the model, the region 1 field aligned current and ring current energy is compared to the westward auroral electrojet AL index and equatorial geomagnetic disturbance storm time Dst index. The WINDMI model is used to analyze the magnetosphere-ionosphere system during major geomagnetic storms and substorms which are community campaign events. Numerical experiments using the WINDMI model are also used to assess the question of how much interplanetary shock events contribute to the geoeffectiveness of solar wind drivers. For two major geomagnetic storm intervals, it is found that the magnetic field compressional jump is important to producing the changes in the AL index. Further, the WINDMI model is implemented to compute model AL and Dst predictions every ten minutes using real-time solar wind data from the ACE satellite as input. Real-Time WINDMI has been capturing substorm and storm activity, as characterized by the AL and Dst indices, reliably since February 2006 and is validated by comparison with ground-based measurements of the indices. Model results are compared for three different candidate input solar wind driving voltage formulas. Modeling of the Dst index is further developed to include the additional physical processes of tail current increases and sudden commencement. A new model, based on WINDMI, is developed using the dayside magnetopause and magnetosphere current systems to model the magnetopause boundary motion and the dayside region 1 field aligned current which is comparable to the auroral upper AU index. / text

The genetic algorithm (GA) is a popular random search and optimization method inspired by the concepts of crossover, random mutation, and natural selection from evolutionary biology. The real-valued genetic algorithm (RGA) is an improved version of the genetic algorithm designed for direct operation on real-valued variables. In this work, a modified version of a genetic algorithm is introduced, which is called a modified genetic algorithm with micro-movement (MGAM). It implements a particle swarm optimization(PSO)-inspired micro-movement phase that helps to improve the convergence rate, while employing the e'cient GA mechanism for maintaining population diversity. In order to test the capability of the MGAM, we firrst implement it on five generally used test functions. Then we test the MGAM on two typical nonlinear dynamical systems. The performance of the MGAM is compared to a basic RGA on all these applications. Finally, we implement the MGAM on the most important application, which is the plasma physics-based model of the solar wind-driven magnetosphere-ionosphere system (WINDMI). In order to use this model for real-time prediction of geomagnetic activity, the model parameters require up-dating every 6-8 hours. We use the MGAM to train the parameters of the model in order to achieve the lowest mean square error (MSE) against the measured auroral electrojet (AL) and Dst indices. The performance of the MGAM is compared to the RGA on historical geomagnetic storm datasets. While the MGAM performs substantially better than the RGA when evaluating standard test functions, the improvement is about 6-12 percent when used on the 20D nonlinear dynamical WINDMI model.