A Novel Binary Particle Swarm Optimization with Feedback for Synthesizing Thinned Planar Arrays

Abstract

—In this paper, we propose a novel binary particle swarm optimization (BPSO) algorithm for the synthesis of thinned planar arrays. The major objective is to diversify the swarm population to discourage premature convergence and enable the swarm to evolve along the proper trend. To these ends, we adopt two levels of feedbacks. First, the error information between the current position and the previous global best is exploited to update a particle's velocity after a mutation operation, which results in a BPSO algorithm with adaptive velocity (BPSOadv). Second, the error between the desired beam-pattern and the current evolution result is fed back as a global information, leading to the BPSO with both adaptive velocity and global information feedback (BPSOadv-GF). We compare our algorithms with several recently published synthesis procedures, and show that there are significant improvements, such as lower sidelobe level and higher reliability.