A comparison of particle swarm optimization and genetic algorithms for a phased array synthesis problem

Abstract

Particle swarm optimization is a recently invented high-performance optimizer that possesses several highly desirable attributes, including the fact that the basic algorithm is very easy to understand and implement. It is similar in some ways to genetic algorithms or evolutionary algorithms, but generally requires only a few lines of code. A particle swarm optimizer is implemented and compared to a genetic algorithm for phased array synthesis of a far field sidelobe notch, using amplitude-only, phase-only, and complex tapering. The results show that particle swarm optimization performs better in some cases while genetic algorithms perform better in others, which implies that the two methods traverse the problem hyperspace differently. Although simple, the particle swarm optimizer shows good possibilities for electromagnetic optimization.