Optimal Pattern synthesis of linear antenna array using Ant Hill Colonization Optimization algorithm(AHCOA)

TL;DR

This paper introduces the Ant Hill Colonization Optimization Algorithm (AHCOA), a new nature-inspired metaheuristic, and demonstrates its effectiveness in optimizing linear antenna array patterns with lower side lobe levels compared to existing algorithms.

Contribution

The paper presents a novel metaheuristic algorithm inspired by ant hill hierarchy and applies it to antenna array pattern synthesis, showing improved performance over other nature-inspired algorithms.

Findings

AHCOA achieves lower side lobe levels in antenna arrays.

AHCOA outperforms ant lion optimizer in pattern synthesis.

The algorithm effectively handles constrained optimization problems.

Abstract

The aim of this paper is to introduce AHCOA to the electromagnetic and antenna community. AHCOA is a new nature inspired meta heuristic algorithm inspired by how there is a hierarchy and departments in the ant hill colonization. It has high probabilistic potential in solving not only unconstrained but also constrained optimization problems. In this paper the AHCOA is applied to linear antenna array for better pattern synthesis in the following ways : By uniform excitation considering equal spacing of the antenna elements with respect to the uniform array. AHCOA is used in obtaining an array pattern to achieve minimum side lobe levels. The results are compared to other state of the art nature based algorithms such as ant lion optimizer, which show a considerable improvement in AHCOA.