Design of very thin wide band absorbers using modified local best particle swarm optimization

Abstract A method of using particle swarm optimization (PSO) algorithm to design electromagnetic absorber is presented. To demonstrate effectiveness of the PSO algorithm three different design cases are optimized. To reduce the local minimum traps, a modified local search strategy is employed. Each design problem is optimized using genetic algorithm (GA) and four variants of PSO algorithms, namely global PSO (gbest), local PSO (lbest), comprehensive learning PSO (CLPSO), and modified local PSO (MLPSO). The results clearly show that the MLPSO is a robust, fast, and useful optimization tool for designing absorbers. A seven-layer absorber achieved by this method has reflection coefficient below 18.7 dB from VHF to 20 GHz.

[1]  Alan Tennant,et al.  Optimised design of Jaumann radar absorbing materials using a genetic algorithm , 1996 .

[2]  Yahya Rahmat-Samii,et al.  RCS reduction of canonical targets using genetic algorithm synthesized RAM , 2000 .

[3]  Jing J. Liang,et al.  Design of Yagi-Uda antennas using comprehensive learning particle swarm optimisation , 2005 .

[4]  D.W. Boeringer,et al.  Efficiency-constrained particle swarm optimization of a modified bernstein polynomial for conformal array excitation amplitude synthesis , 2005, IEEE Transactions on Antennas and Propagation.

[5]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[6]  K. Suetake,et al.  Application of Ferrite to Electromagnetic Wave Absorber and its Characteristics , 1970 .

[7]  Andries P. Engelbrecht,et al.  Computational Intelligence: An Introduction , 2002 .

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

[9]  Jose Perini,et al.  Design of broad-band radar-absorbing materials for large angles of incidence , 1993 .

[10]  D.H. Werner,et al.  Particle swarm optimization versus genetic algorithms for phased array synthesis , 2004, IEEE Transactions on Antennas and Propagation.

[11]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[12]  John F. Shaeffer,et al.  Radar Cross Section , 2004 .

[13]  D.S. Weile,et al.  Application of a parallel particle swarm optimization scheme to the design of electromagnetic absorbers , 2005, IEEE Transactions on Antennas and Propagation.

[14]  H. Komari,et al.  Wide band electromagnetic wave absorber with thin magnetic layers , 1994, IEEE Trans. Broadcast..

[15]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[16]  Y. Rahmat-Samii,et al.  Particle swarm optimization in electromagnetics , 2004, IEEE Transactions on Antennas and Propagation.

[17]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).