Improved Dipper-Throated Optimization for Forecasting Metamaterial Design Bandwidth for Engineering Applications

Abstract

Metamaterials have unique physical properties. They are made of several elements and are structured in repeating patterns at a smaller wavelength than the phenomena they affect. Metamaterials’ exact structure, geometry, size, orientation, and arrangement allow them to manipulate electromagnetic waves by blocking, absorbing, amplifying, or bending them to achieve benefits not possible with ordinary materials. Microwave invisibility cloaks, invisible submarines, revolutionary electronics, microwave components, filters, and antennas with a negative refractive index utilize metamaterials. This paper proposed an improved dipper throated-based ant colony optimization (DTACO) algorithm for forecasting the bandwidth of the metamaterial antenna. The first scenario in the tests covered the feature selection capabilities of the proposed binary DTACO algorithm for the dataset that was being evaluated, and the second scenario illustrated the algorithm’s regression skills. Both scenarios are part of the studies. The state-of-the-art algorithms of DTO, ACO, particle swarm optimization (PSO), grey wolf optimizer (GWO), and whale optimization (WOA) were explored and compared to the DTACO algorithm. The basic multilayer perceptron (MLP) regressor model, the support vector regression (SVR) model, and the random forest (RF) regressor model were contrasted with the optimal ensemble DTACO-based model that was proposed. In order to assess the consistency of the DTACO-based model that was developed, the statistical research made use of Wilcoxon’s rank-sum and ANOVA tests.

Machine Intelligence in Metamaterials Design

“…Alharbi et 1651 al. for example recently proposed a Dipper-Throated (DT) 1652 optimization for ACO (DTACO) [193] as a viable method 1653 for predicting the bandwidth of electromagnetic metama-1654 terials, which in turn is an enabler for the manipulation 1655 of bending, absorbing or amplification of electromagnetic 1656 waves. DT optimization is essentially one that assumes 1657 birds fly and swim to find food and the optimization takes 1658 into account bird numbers, velocities, and positions.…”