A proposal for a universal parameter configuration for genetic algorithm optimization of electromagnetic devices

Genetic algorithms (GAs) are widely used in the optimization of electromagnetic devices. However, if finite element analyses are needed for the evaluation of the objective function, GAs require a long computation time. They also need a difficult tuning session before starting optimization, to select the best configuration of the parameters that control the algorithm. This paper proposes a universal choice of the GA parameters in order to spare the designer the tuning session and to reduce the computing time for the whole procedure.

[1]  E. M. Freeman,et al.  The direct calculation of global quantities from an FE formulation for the optimization of power frequency electromagnetic devices , 1998 .

[2]  Salvatore Coco,et al.  Charge iteration: A procedure for the finite element computation of unbounded electrical fields , 1994 .

[3]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[4]  Salvatore Coco,et al.  A theoretical study of charge iteration , 1996 .

[5]  Salvatore Coco,et al.  Finite element iterative solution of skin effect problems in open boundaries , 1996 .

[6]  Giovanni Aiello,et al.  Stochastic optimization of an electromagnetic actuator by means of Dirichlet boundary condition iteration , 2000 .

[7]  Kalyanmoy Deb,et al.  A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.

[8]  Song-Yop Hahn,et al.  A study on comparison of optimization performances between immune algorithm and other heuristic algorithms , 1998 .

[9]  Stephan Russenschuck,et al.  Synthesis, inverse problems and optimization in computational electromagnetics , 1996 .

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

[11]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[12]  D. E. Goldberg,et al.  Genetic Algorithms in Search, Optimization & Machine Learning , 1989 .

[13]  J.-L. Coulomb,et al.  Nonlinear optimization methods applied to magnetic actuators design , 1992 .

[14]  Kazufumi Kaneda,et al.  Performance Comparison Between Gray Coded and Binary Coded Genetic Algorithms for Inverse Shape Optimization of Magnetic Devicec , 1998 .

[15]  Maurizio Repetto,et al.  Stochastic algorithms in electromagnetic optimization , 1998 .

[16]  P. Di Barba,et al.  CAD and optimization techniques , 2000 .

[17]  Dirk Thierens,et al.  Convergence Models of Genetic Algorithm Selection Schemes , 1994, PPSN.