Optimal Pattern synthesis of linear antenna array using Ant Hill
Colonization Optimization algorithm(AHCOA)
- URL: http://arxiv.org/abs/2207.04046v1
- Date: Wed, 6 Jul 2022 10:22:05 GMT
- Title: Optimal Pattern synthesis of linear antenna array using Ant Hill
Colonization Optimization algorithm(AHCOA)
- Authors: Sunit Shantanu Digamber Fulari, Harbinder Singh
- Abstract summary: AHCOA is a new nature inspired meta algorithm inspired by how there is a hierarchy and departments in the ant hill.
It has high probabilistic potential in solving unconstrained but also constrained optimization problems.
- Score: 1.588193964339148
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- A Gradient Meta-Learning Joint Optimization for Beamforming and Antenna Position in Pinching-Antenna Systems [63.213207442368294]
We consider a novel optimization design for multi-waveguide pinching-antenna systems.<n>The proposed GML-JO algorithm is robust to different choices and better performance compared with the existing optimization methods.
arXiv Detail & Related papers (2025-06-14T17:35:27Z) - ABCO: Adaptive Bacterial Colony Optimisation [3.031375888004876]
This paper introduces a new optimisation algorithm, called Adaptive Bacterial Colony optimisation (ABCO)<n>ABCO follows three stages--explore, exploit and reproduce--and is adaptable to meet the requirements of its applications.
arXiv Detail & Related papers (2025-05-02T14:48:14Z) - Aerial Secure Collaborative Communications under Eavesdropper Collusion in Low-altitude Economy: A Generative Swarm Intelligent Approach [84.20358039333756]
We introduce distributed collaborative beamforming (DCB) into AAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions.
We minimize the two-way known secrecy capacity and maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers.
We propose a novel generative swarm intelligence (GenSI) framework to solve the problem with less overhead.
arXiv Detail & Related papers (2025-03-02T04:02:58Z) - A novel algorithm for optimizing bundle adjustment in image sequence alignment [6.322876598831792]
This paper introduces a novel algorithm for optimizing the Bundle Adjustment (BA) model in the context of image sequence alignment for cryo-electron tomography.
Extensive experiments on both synthetic and real-world datasets were conducted to evaluate the algorithm's performance.
arXiv Detail & Related papers (2024-11-10T03:19:33Z) - Multi-objective Memetic Algorithm with Adaptive Weights for Inverse Antenna Design [0.0]
modification of a single-objective algorithm into its multi-objective counterpart.
Result is a considerable increase in speed in the order of tens to hundreds.
arXiv Detail & Related papers (2024-08-07T08:43:38Z) - Ant Colony Sampling with GFlowNets for Combinatorial Optimization [68.84985459701007]
Generative Flow Ant Colony Sampler (GFACS) is a novel meta-heuristic method that hierarchically combines amortized inference and parallel search.
Our method first leverages Generative Flow Networks (GFlowNets) to amortize a multi-modal prior distribution over a solution space.
arXiv Detail & Related papers (2024-03-11T16:26:06Z) - ELRA: Exponential learning rate adaption gradient descent optimization
method [83.88591755871734]
We present a novel, fast (exponential rate), ab initio (hyper-free) gradient based adaption.
The main idea of the method is to adapt the $alpha by situational awareness.
It can be applied to problems of any dimensions n and scales only linearly.
arXiv Detail & Related papers (2023-09-12T14:36:13Z) - GA-Aided Directivity in Volumetric and Planar Massive-Antenna Array
Design [0.0]
The problem of directivity enhancement, leading to the increase in the directivity gain over a certain desired angle of arrival/departure (AoA/AoD) is considered in this work.
A new volumetric array of the directivity array is proposed using rectangular rectangular angles and a generalzimuth elevation pattern.
Such a directivity distance is formulated to achieve as high directivity gains as possible.
arXiv Detail & Related papers (2023-01-07T21:52:19Z) - Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization [108.35402316802765]
We propose a new first-order optimization algorithm -- AcceleratedGradient-OptimisticGradient (AG-OG) Ascent.
We show that AG-OG achieves the optimal convergence rate (up to a constant) for a variety of settings.
We extend our algorithm to extend the setting and achieve the optimal convergence rate in both bi-SC-SC and bi-C-SC settings.
arXiv Detail & Related papers (2022-10-31T17:59:29Z) - Ant Hill Colonization optimization algorithm(AHCOA) for controlling the
side lobe of a uniform linear array [1.588193964339148]
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.
arXiv Detail & Related papers (2022-06-22T16:44:37Z) - ANA: Ant Nesting Algorithm for Optimizing Real-World Problems [21.95618652596178]
A novel intelligent swarm is proposed called ant nesting algorithm (ANA)
The algorithm is inspired by Leptothorax ants and mimics the behavior of ants searching for positions to deposit grains while building a new nest.
ANA is considered a continuous algorithm that updates the search agent position by adding the rate of change.
arXiv Detail & Related papers (2021-12-04T08:55:06Z) - NOMA in UAV-aided cellular offloading: A machine learning approach [59.32570888309133]
A novel framework is proposed for cellular offloading with the aid of multiple unmanned aerial vehicles (UAVs)
Non-orthogonal multiple access (NOMA) technique is employed at each UAV to further improve the spectrum efficiency of the wireless network.
A mutual deep Q-network (MDQN) algorithm is proposed to jointly determine the optimal 3D trajectory and power allocation of UAVs.
arXiv Detail & Related papers (2020-10-18T17:38:48Z) - Multi-View Spectral Clustering with High-Order Optimal Neighborhood
Laplacian Matrix [57.11971786407279]
Multi-view spectral clustering can effectively reveal the intrinsic cluster structure among data.
This paper proposes a multi-view spectral clustering algorithm that learns a high-order optimal neighborhood Laplacian matrix.
Our proposed algorithm generates the optimal Laplacian matrix by searching the neighborhood of the linear combination of both the first-order and high-order base.
arXiv Detail & Related papers (2020-08-31T12:28:40Z) - Non-Adaptive Adaptive Sampling on Turnstile Streams [57.619901304728366]
We give the first relative-error algorithms for column subset selection, subspace approximation, projective clustering, and volume on turnstile streams that use space sublinear in $n$.
Our adaptive sampling procedure has a number of applications to various data summarization problems that either improve state-of-the-art or have only been previously studied in the more relaxed row-arrival model.
arXiv Detail & Related papers (2020-04-23T05:00:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.