Ant Lion Optimization algorithm to control side lobe level and null depths in linear antenna arrays

Abstract

The aim of this paper is to introduce the Ant Lion Optimization (ALO) algorithm to the electromagnetics and antenna community. ALO is a new nature-inspired algorithm mimicking the hunting behaviour of antlions. It is used to solve unconstrained as well as constrained optimization problems with diverse search spaces. One of the advantages of ALO is that it has very few parameters to tune, making it a flexible algorithm for solving diverse problems. In this paper, ALO has been applied for antenna current as well as antenna position optimization for pattern synthesis of linear antenna arrays. The potential of ALO to perform linear array beamsteering by optimizing the antenna phases is also illustrated. Different design examples are presented that illustrate the use of ALO for linear array optimization so as to obtain an array pattern with minimum side lobe level (SLL) along with null placement in the specified directions. Close-in SLL minimization for optimal pattern synthesis is also presented. The obtained results are validated by benchmarking with results obtained using other state-of-the-art nature-inspired metaheuristic algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO), cat swarm optimization (CSO) and biogeography based optimization (BBO). The results suggest that optimization of linear antenna arrays using ALO provides considerable enhancements compared to the uniform array and the synthesis obtained from other optimization techniques. This demonstrates the potential of ALO as a strong candidate to be used for antenna array synthesis and other electromagnetic optimization problems.