Hybrid genetic algorithm for electromagnetic topology optimization

This paper proposes a hybrid genetic algorithm (GA) for electromagnetic topology optimization. A two-dimensional (2-D) encoding technique, which considers the geometrical topology, is first applied to electromagnetics. Then, a 2-D geographic crossover is used as the crossover operator. A novel local optimization algorithm, called the on/off sensitivity method, hybridized with the 2-D encoded GA, improves the convergence characteristics. The algorithm was verified by applying it to various case studies, and the results are presented herein.

[1]  Jin-kyu Byun,et al.  Topology optimization of electrical devices using material energy and sensitivity , 1999, IEEE International Magnetics Conference.

[2]  Shiyou Yang,et al.  A self-learning simulated annealing algorithm for global optimizations of electromagnetic devices , 2000 .

[3]  Jin-Kyu Byun,et al.  Faster calculation of sensitivity in the source current distribution problem using reciprocity theorem , 2001 .

[4]  Byung Ro Moon,et al.  A Two-Dimensional Embedding of Graphs for Genetic Algorithms , 1997, ICGA.

[5]  L. Darrell Whitley,et al.  Lamarckian Evolution, The Baldwin Effect and Function Optimization , 1994, PPSN.

[6]  James P. Cohoon,et al.  Genetic Placement , 1987, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[7]  Jean-Francois Mangin,et al.  A multiresolution framework to MEG/EEG source imaging , 2001, IEEE Trans. Biomed. Eng..

[8]  Hyun-Kyo Jung,et al.  Optimization of the coil shape in deflection yoke considering practical coil winding processes , 2002 .

[9]  Geoffrey E. Hinton,et al.  How Learning Can Guide Evolution , 1996, Complex Syst..

[10]  Jin-Kyu Byun,et al.  Inverse problem application of topology optimization method with mutual energy concept and design sensitivity , 2000 .

[11]  Byung Ro Moon,et al.  On Multi-Dimensional Encoding/Crossover , 1995, ICGA.

[12]  David A. Lowther,et al.  A comparison of MRI magnet design using a Hopfield network and the optimized material distribution method , 1998 .

[13]  A. Dale,et al.  Improved Localizadon of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach , 1993, Journal of Cognitive Neuroscience.

[14]  Shiyou Yang,et al.  An improved Tabu search for the global optimizations of electromagnetic devices , 2001 .

[15]  José Márcio Machado,et al.  Wavelet-Galerkin method for computations of electromagnetic fields-computation of connection coefficients , 2000 .

[16]  Jennifer Ryan,et al.  A Two-Dimensional Genetic Algorithm for the Ising Problem , 1991, Complex Syst..

[17]  Hyun-Kyo Jung,et al.  Niching genetic algorithm adopting restricted competition selection combined with pattern search method , 2002 .

[18]  Ki Jin Han,et al.  Optimal core shape design for cogging torque reduction of brushless DC motor using genetic algorithm , 2000 .

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

[20]  Song-Yop Hahn,et al.  Topology optimization of electrostatic actuator using design sensitivity , 2002 .

[21]  Yun-Sik Lee,et al.  GEORG: VLSI circuit partitioner with a new genetic algorithm framework , 1998, J. Intell. Manuf..