Efficient Antenna Optimization Using a Hybrid of Evolutionary Programing and Particle Swarm Optimization

TL;DR

This paper introduces a hybrid optimization algorithm combining Evolutionary Programming and Particle Swarm Optimization to improve the efficiency of global antenna array and metasurface design, outperforming single-method approaches.

Contribution

The paper presents a novel hybrid EP-PSO algorithm that integrates swarm directions into evolutionary programming for enhanced antenna optimization.

Findings

Hybrid EP-PSO outperforms EP-only and PSO-only methods.

Effective in reducing side-lobe levels in antenna arrays.

Improves beam shaping of metasurfaces.

Abstract

In this paper, we present a hybrid of Evolutionary Programming (EP) and Particle Swarm Optimization (PSO) algorithms for numerically efficient global optimization of antenna arrays and metasurfaces. The hybrid EP-PSO algorithm uses an evolutionary optimization approach that incorporates swarm directions in the standard self-adaptive EP algorithm. As examples, we have applied this hybrid technique to two antenna problems: the side-lobe-level reduction of a non-uniform spaced (aperiodic) linear array and the beam shaping of a printed antenna loaded with a partially reflective metasurface. Detailed comparisons between the proposed hybrid EP-PSO technique and EP-only and PSO-only techniques are given, demonstrating the efficiency of this hybrid technique in the complex antenna design problems.