Shape-Preserving Response Prediction for Microwave Design Optimization

A shape-preserving response prediction methodology for microwave design optimization is introduced. The presented technique allows us to estimate the response of the microwave structure being optimized (fine model) using a computationally cheap representation of the structure (coarse model). The change of the coarse model response is described by the translation vectors corresponding to certain (finite) number of characteristic points of the response. These translation vectors are subsequently used to predict the response change of the fine model. The presented method has very good generalization capability and it is not based on any extractable parameters, which makes it easy to implement. Applications for microwave design optimization are discussed. The robustness of the proposed approach is demonstrated by extensive comparison with space mapping, which is one of the most efficient optimization approaches in microwave engineering so far.

[1]  R. Lewis,et al.  An Overview of First-Order Model Management for Engineering Optimization , 2001 .

[2]  Shih-Ming Wang,et al.  Compact and wideband microstrip bandstop filter , 2005 .

[3]  Elena A. Lomonova,et al.  Space Mapping Optimization of a Cylindrical Voice Coil Actuator , 2006 .

[4]  Domenico Lahaye,et al.  Space Mapping and Defect Correction , 2008 .

[5]  John W. Bandler,et al.  Space mapping technique for electromagnetic optimization , 1994 .

[6]  Slawomir Koziel Efficient optimization of microwave circuits using shape-preserving response prediction , 2009, 2009 IEEE MTT-S International Microwave Symposium Digest.

[7]  D. Smith,et al.  EM-based design of large-scale dielectric-resonator filters and multiplexers by space mapping , 2004, IEEE Transactions on Microwave Theory and Techniques.

[8]  Zhewang Ma,et al.  A microstrip dual‐band bandpass filter with reduced size and improved stopband characteristics , 2008 .

[9]  S. Koziel,et al.  Space-Mapping Optimization With Adaptive Surrogate Model , 2007, IEEE Transactions on Microwave Theory and Techniques.

[10]  P. W. Hemker,et al.  Space Mapping and Defect Correction , 2005 .

[11]  M. Dorica,et al.  Response surface space mapping for electromagnetic optimization , 2006, IEEE Transactions on Magnetics.

[12]  Marcus Redhe,et al.  Using space mapping and surrogate models to optimize vehicle crashworthiness design , 2002 .

[13]  S. Amari,et al.  Space-mapping optimization of planar coupled-resonator microwave filters , 2006, IEEE Transactions on Microwave Theory and Techniques.

[14]  J.W. Bandler,et al.  Implicit space mapping optimization exploiting preassigned parameters , 2004, IEEE Transactions on Microwave Theory and Techniques.

[15]  A. J. Booker,et al.  A rigorous framework for optimization of expensive functions by surrogates , 1998 .

[16]  J.W. Bandler,et al.  Space Mapping With Adaptive Response Correction for Microwave Design Optimization , 2009, IEEE Transactions on Microwave Theory and Techniques.

[17]  S. Koziel,et al.  A Space-Mapping Framework for Engineering Optimization—Theory and Implementation , 2006, IEEE Transactions on Microwave Theory and Techniques.

[18]  N. M. Alexandrov,et al.  A trust-region framework for managing the use of approximation models in optimization , 1997 .

[19]  A. Hoorfar Evolutionary Programming in Electromagnetic Optimization: A Review , 2007, IEEE Transactions on Antennas and Propagation.

[20]  J.W. Bandler,et al.  EM-based surrogate modeling and design exploiting implicit, frequency and output space mappings , 2003, IEEE MTT-S International Microwave Symposium Digest, 2003.

[21]  J.S. Fu,et al.  Particle swarm optimization and finite-element based approach for microwave filter design , 2005, IEEE Transactions on Magnetics.

[22]  Andy J. Keane,et al.  A Constraint Mapping Approach to the Structural Optimization of an Expensive Model using Surrogates , 2001 .

[23]  Jen-Tsai Kuo,et al.  Parallel-coupled microstrip filters with over-coupled end stages for suppression of spurious responses , 2003 .

[24]  V. Gutierrez-Ayala,et al.  EM-Based Monte Carlo Analysis and Yield Prediction of Microwave Circuits Using Linear-Input Neural-Output Space Mapping , 2006, IEEE Transactions on Microwave Theory and Techniques.

[25]  Ke-Li Wu,et al.  An effective dynamic coarse model for optimization design of LTCC RF circuits with aggressive space mapping , 2004, IEEE Transactions on Microwave Theory and Techniques.

[26]  J.W. Bandler,et al.  Space mapping: the state of the art , 2004, IEEE Transactions on Microwave Theory and Techniques.

[27]  Song-Yop Hahn,et al.  A new design technique of magnetic systems using space mapping algorithm , 2001 .

[28]  John W. Bandler,et al.  Quality assessment of coarse models and surrogates for space mapping optimization , 2008 .

[29]  Raphael T. Haftka,et al.  Surrogate-based Analysis and Optimization , 2005 .

[30]  Shyh-Kang Jeng,et al.  Compact microstrip dual-band bandpass filters design using genetic-algorithm techniques , 2006, IEEE Transactions on Microwave Theory and Techniques.

[31]  Y. Rahmat-Samii,et al.  Analysis and Particle Swarm Optimization of Correlator Antenna Arrays for Radio Astronomy Applications , 2008, IEEE Transactions on Antennas and Propagation.