  This paper aims to introduce the Ant hill colonization optimization
algorithm(AHCOA) to the electromagnetics and antenna community. The ant hill is
built by special species of ants known as formicas ants(also meadow ants, fire
ants and harvester ants). AHCOA is a novel new nature inspired algorithm
mimicking how the ants built and sustain the ant hill for their survival and
sustenance for many years. This problem solves constrained and unconstrained
optimization problems with wide capability in diverse fields. AHCOA is used by
writing equations of volumetric analysis of the ant hill mould the manner in
which the structure is architected. In this paper, we have shown how AHCOA is
better than the previous paper on ant lion optimizer for controlling side lobe
in antenna pattern synthesis in paper [1]. The potential of AHCOA in
synthesizing and analyzing for d/ varying from 1.1,0.6,0.5,0.3 and 0.1 linear
array is also illustrated. Antenna side lobe level minimization is compared
with ant lion optimizer showing why AHCOA is better than the previously
simulated ant lion optimizer for side lobe control. The results show why linear
arrays are better synthesized for AHCOA then other algorithms used in planar
arrays. This paper shows why AHCOA is a strong candidate for antenna
optimization used in linear arrays.

Neural and Evolutionary Computing