Coupling matrix synthesis of cross-coupled microwave filters using a hybrid optimisation algorithm

A hybrid optimisation algorithm that synthesises coupling matrices for cross-coupled microwave filters is presented. A binary encoded genetic algorithm is combined at regular intervals with a sequential quadratic programming local search method to form a hybrid, exploiting the speed of the local search, while maintaining diversity with the genetic algorithm. The genetic algorithm uses the stochastic uniform selection technique and a multiple point crossover operator. A compact, efficient cost function requiring only the determinant and a cofactor of the coupling matrix is used as the basis of the optimisation algorithm. Optimisation algorithms simplify the process of synthesising coupling matrices, compared with analytical synthesis. However, algorithms that use only local search methods cannot be guaranteed to find a global minimum. This hybrid method aims to extend the range of coupling matrices that can be synthesised by optimisation, while maintaining the speed of search. A coupling matrix for a tenth order coupling matrix for a dual band symmetric filter and a seventh order asymmetric filter are synthesised to verify the method.

[1]  A. E. Atia,et al.  Narrow-band multiple-coupled cavity synthesis , 1974 .

[2]  Dominique Baillargeat,et al.  Direct electromagnetic optimization of microwave filters , 2001 .

[3]  Konstantinos G. Margaritis,et al.  An experimental study of benchmarking functions for genetic algorithms , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[4]  David B. Fogel,et al.  An introduction to simulated evolutionary optimization , 1994, IEEE Trans. Neural Networks.

[5]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[6]  Jia-Sheng Hong,et al.  Microstrip filters for RF/microwave applications , 2001 .

[7]  S. Amari,et al.  Synthesis of cross-coupled resonator filters using an analytical gradient-based optimization technique , 2000 .

[8]  Dominique Baillargeat,et al.  Direct electromagnetic optimisation method for microwave filter design , 1999 .

[9]  G. Macchiarella,et al.  Design techniques for dual-passband filters , 2005, IEEE Transactions on Microwave Theory and Techniques.

[10]  Manseok Uhm,et al.  Synthesis of Resonator Filters With Arbitrary Topology Using Hybrid Method , 2007, IEEE Transactions on Microwave Theory and Techniques.

[11]  Peter J. B. Hancock,et al.  An Empirical Comparison of Selection Methods in Evolutionary Algorithms , 1994, Evolutionary Computing, AISB Workshop.

[12]  Paul T. Boggs,et al.  Sequential Quadratic Programming , 1995, Acta Numerica.

[13]  P. Kozakowski,et al.  Quadratic Programming Approach to Coupled Resonator Filter CAD , 2006, IEEE Transactions on Microwave Theory and Techniques.

[14]  R. Cameron.  General coupling matrix synthesis methods for Chebyshev filtering functions , 1999 .

[15]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[16]  Jean-Michel Renders,et al.  Hybrid methods using genetic algorithms for global optimization , 1996, IEEE Trans. Syst. Man Cybern. Part B.