The Grey Wolf Optimizer and Its Applications in Electromagnetics

The grey wolf optimizer (GWO) is a newly developed swarm intelligence-based optimization technique that mimics the social hierarchy and group hunting behavior of grey wolves in nature. Here, a detailed introduction of the GWO algorithm is given, after which, three sets of examples are investigated: first, numerical experiments on four benchmark functions are conducted; second, the GWO is applied to the synthesis of linear arrays with the aim of reducing the peak sidelobe level under various constraints; and finally, the performance of the GWO is further verified on the optimization design of two representative antennas, namely, a dual-band E-shaped patch antenna and a wideband magneto-electric dipole antenna. The results show that the GWO is capable of outperforming or providing very competitive results compared with some well-known metaheuristics such as the genetic algorithm, particle swarm optimization, and differential evolution. Thus, it may serve as a promising candidate for handling electromagnetic problems.

I. Introduction

Nature-inspired optimization techniques emerged in the past decades and became very popular in various fields. They have been shown to be effective in dealing with difficult nonconvex and multidimensional problems in engineering and science. Some well-known nature-inspired optimization algorithms include the genetic algorithm (GA) [1], particle swarm optimization (PSO) [2], ant colony optimization (ACO) [3], differential evolution (DE) [4], etc. In general, each algorithm has its strengths and weaknesses, and according to the no free lunch theorem [5], there is no special optimization technique that possesses superior performance on the whole set of optimization problems. Hence, research on improvement of the existing optimization techniques as well as proposing new optimization techniques has become very active recently.