Space mapping with inverse difference technique for reconfigurable antenna design problem

In surrogate based optimization techniques, time consuming but accurate fine model is used along with less accurate but fast coarse model to reduce the overall computational effort. In this work, space mapping with inverse difference technique that is one of the space mapping based optimization techniques based on surrogate optimization is applied to antenna design problem. This technique provides efficient strategy to reduce computational effort while improving the convergence. Inverse difference mapping is constituted in terms of difference knowledge obtained by input and output of problem space. In addition inverse coarse model that is obtained by feed forward multi layer perceptron can generate necessary knowledge to form inverse mapping from coarse model input space to fine model input space instead of parameter extraction process for same knowledge. The efficiency of space mapping with inverse difference technique will be demonstrated by reconfigurable antenna design example in terms of its convergence and accuracy.