Constrained Planar Array Thinning Based on Discrete Particle Swarm Optimization with Hybrid Search Strategies

This article presents a novel optimization algorithm for large array thinning. The algorithm is based on Discrete Particle Swarm Optimization (DPSO) integrated with some different search strategies. It utilizes a global learning strategy to improve the diversity of populations at the early stage of optimization. A dispersive solution set and the gravitational search algorithm are used during particle velocity updating. Then, a local search strategy is enabled in the later stage of optimization. The particle position is adaptively adjusted by the mutation probability, and its motion state is monitored by two observation parameters. The peak side-lobe level (PSLL) performance, effectiveness and robustness of the improved PSO algorithm are verified by several representative examples.

Automated summary

This article introduces a novel optimization algorithm for large array thinning, based on DPSO and integrated with various search strategies. It utilizes a global learning strategy and a local search strategy to improve optimization performance at different stages, and the effectiveness and robustness of the algorithm are verified through several examples.