Comparison of different hybridization strategies in evolutionary optimization for EM

The genetical swarm optimization (GSO) is the integration of the genetic algorithm (GA) and particle swarm optimization (PSO). The key feature of this algorithm is that it maintains the integration of GA and PSO for the entire run. In this paper the authors present a comparison of the GSO and different hybridization strategies, in order to explore in the most effective way the properties of the evolutionary approaches now in use for the optimization of EM structures, and to validate the performances of their hybrid procedure. Some results of the tested algorithm are shown in the design optimization of a linear array antenna

[1]  Gabriela Ciuprina,et al.  Use of intelligent-particle swarm optimization in electromagnetics. IEEE Trans Mag , 2002 .

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

[3]  Y. Rahmat-Samii,et al.  Particle swarm, genetic algorithm, and their hybrids: optimization of a profiled corrugated horn antenna , 2002, IEEE Antennas and Propagation Society International Symposium (IEEE Cat. No.02CH37313).

[4]  Chia-Feng Juang,et al.  A hybrid of genetic algorithm and particle swarm optimization for recurrent network design , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  F. Grimaccia,et al.  A new hybrid evolutionary algorithm for high dimension electromagnetic problems , 2005, 2005 IEEE Antennas and Propagation Society International Symposium.

[6]  Yahya Rahmat-Samii,et al.  Electromagnetic Optimization by Genetic Algorithms , 1999 .