ABSTRACT
Reconfigurable antennas play a crucial role in contemporary wireless communication. Recent technological advances manifest the need for optimal antenna design. Optimization algorithms have been gaining in popularity over the past two decades due to their adaptability and efficiency. These algorithms are the keys to designing a compact and highly efficient antenna. Moreover, the implementation of optimization algorithms in antenna design thrives on better performance characteristics. The optimization technique adopted is governed by the parameters and the application specifications. This chapter depicts a technical review of commonly used optimization algorithms for performance enhancement in reconfigurable antennas. Algorithms like genetic algorithm (GA), differential evolution (DE), covariance matrix adaptation (CMA), particle swarm optimization (PSO), and grey wolf algorithm (GWA) are explored. A brief discussion of each optimization technique, the parameters associated with optimization metrics for performance enhancement, and the advantages and limitations of each concerning antenna are provided. The purpose of this review is to give the reader a quick rundown of the different algorithms to help them select the best strategy for an antenna design.
