Application of a comprehensive learning particle swarm optimizer to unequally spaced linear array synthesis with sidelobe level suppression and null control

We present unequally spaced linear array synthesis with sidelobe suppression under constraints to beamwidth and null control using a design technique based on a Comprehensive Learning Particle Swarm Optimizer (CLPSO). CLPSO utilizes a new learning strategy that achieves the goal to accelerate the convergence of the classical PSO. Numerical examples are compared to the existing array designs in the literature and to those found by the other evolutionary algorithms. The synthesis examples that are presented show that the CLPSO algorithm outperforms the common PSO algorithms and a real-coded genetic algorithm (GA).