Abstract:

Nowadays, wireless communication devices turn out to be transportable owing to the execution of the current technologies. The antenna is the most important component deployed for communication purposes. The antenna plays an imperative role in receiving and transmitting the signals for any sensor network. Among varied antennas, micro strip fractal antenna (MFA) significantly contributes to increasing antenna gain. This study employs a hybrid optimization method known as the elephant clan updated grey wolf algorithm to introduce an optimized MFA design. This method optimizes antenna characteristics, including directivity and gain. Here, the factors, including length, width, ground plane length, height, and feed offset-X and feed offset-Y, are taken into account to achieve the best performance of gain and directivity. Ultimately, the superiority of the suggested technique over state-of-the-art strategies is calculated for various metrics such as cost and gain. The adopted model converges to a minimal value of 0.2872. Further, the spider monkey optimization (SMO) model accomplishes the worst performance over all other existing models like elephant herding optimization (EHO), grey wolf optimization (GWO), lion algorithm (LA), support vector regressor (SVR), bacterial foraging–particle swarm optimization (BF-PSO) and shark smell optimization (SSO). Effective MFA design is obtained using the suggested strategy regarding various parameters.

Key words: micro strip fractal antenna (MFA) model, gain, directivity, support vector regressor (SVR) approach, elephant clan updated grey wolf algorithm (ECU-GWA)