Convergence improvement of surrogate based optimization for reconfigurable antenna design using knowledge based inverse 3-step modeling

Engineering design process requires modeling and optimization to find optimum design parameters. While direct optimization only exploits time consuming but accurate fine model, surrogate based optimization exploits less accurate but fast coarse model to reduce the overall computational effort. In this work, space mapping with inverse difference technique is applied to antenna design problem together with efficient 3-step modeling. The combination of two techniques provides less computational effort and better convergence through the accuracy improvement based on the new inverse 3-step modeling strategy. The inverse coarse model which is used for the parameter extraction process during the optimization is realized by knowledge based inverse 3-step modeling. Inverse 3-step coarse model is obtained by multi layer perceptron in MATLAB ANN toolbox. The efficiency of the combination of space mapping with inverse difference technique and 3-step modeling strategy will be demonstrated by reconfigurable antenna design example in terms of their convergence and accuracy through its multiple operating frequency characteristic.