Ant Hill Colonization optimization algorithm(AHCOA) for controlling the side lobe of a uniform linear array

• URL: http://arxiv.org/abs/2207.02910v1
• Date: Wed, 22 Jun 2022 16:44:37 GMT
• Title: Ant Hill Colonization optimization algorithm(AHCOA) for controlling the side lobe of a uniform linear array
• Authors: Sunit Shantanu Digamber Fulari, Harbinder Singh
• Abstract summary: 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. AHCOA is used by writing equations of volumetric analysis of the ant hill mould the manner in which the structure is architected. This paper shows why AHCOA is a strong candidate for antenna optimization used in linear arrays.
• Score: 1.588193964339148
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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