Craziness particle swarm optimization based hyper beamforming of linear antenna arrays

In this paper, various evolutionary optimization based algorithms like real coded genetic algorithm (RGA), conventional particle swarm optimization (PSO), a proposed craziness particle swarm optimization (CRPSO) have been applied for the optimal design of hyper beamforming of linear antenna array. Hyper beam is derived from sum and difference beam patterns each raised to the power of the hyper beam exponent parameter for the array. CRPSO uses new definition for the velocity vector. The simulation results show CRPSO outperforms RGA and PSO in the optimal hyper beamforming by achieving much greater reduction in sidelobe level (SLL) and much more improved first null beam width (FNBW) keeping the same value of hyper beam exponent. The optimized hyper beam is achieved by optimization of current excitation weights and uniform inter-element spacing. The approach is illustrated through 10-, 14-, and 20-element linear antenna arrays.