Particle swarm optimization in electromagnetics

TLDR

Particle swarm optimization is a robust stochastic evolutionary computation technique inspired by swarm intelligence, recently introduced to the electromagnetics community. This paper provides a conceptual overview and detailed explanation of PSO and demonstrates its application to electromagnetic optimization problems. The authors present an updated PSO framework, discuss recent algorithmic advances and engineering parameter selection, and illustrate its use in optimizing a profiled corrugated horn antenna. Results from the UCLA‑PSO implementation show characteristic swarm behavior, and a boundary‑condition study demonstrates that the invisible wall technique outperforms absorbing and reflecting walls.

Abstract

The particle swarm optimization (PSO), new to the electromagnetics community, is a robust stochastic evolutionary computation technique based on the movement and intelligence of swarms. This paper introduces a conceptual overview and detailed explanation of the PSO algorithm, as well as how it can be used for electromagnetic optimizations. This paper also presents several results illustrating the swarm behavior in a PSO algorithm developed by the authors at UCLA specifically for engineering optimizations (UCLA-PSO). Also discussed is recent progress in the development of the PSO and the special considerations needed for engineering implementation including suggestions for the selection of parameter values. Additionally, a study of boundary conditions is presented indicating the invisible wall technique outperforms absorbing and reflecting wall techniques. These concepts are then integrated into a representative example of optimization of a profiled corrugated horn antenna.