SYNTHESIS OF UNEQUALLY SPACED ANTENNA ARRAYS BY USING INHERITANCE LEARNING PARTICLE SWARM OPTIMIZATION

In this paper, synthesis of unequally spaced linear antenna arrays based on an inheritance learning particle swarm optimization (ILPSO) is presented. In order to improve the optimization efficiency of the PSO algorithm, we propose an inheritance learning strategy that can be applied to different topology of different PSO algorithms. In ILPSO algorithm, each cycle contains several PSO optimization processes, and uniform initial particle positions, part of which inherited from the good results in pre-cycles, are adopted in post-cycles. ILPSO enhances the exploration ability of PSO algorithm significantly, and can escape from the trap of local optimum areas with greater probability. The results demonstrate good performance of the ILPSO in solving a set of eight 30-D benchmark functions when compared to nine other variants of the PSO. The novel proposed algorithm has been applied in 32-element position-only array synthesis with three different constraints, simulation results show that ILPSO obtains better synthesis results reliably and efficiently.