Mutation probability-based lion algorithm for design and optimization of microstrip patch antenna

The advantages and performance of microstrip patch antennas (MPA) namely, reduced weight, reduced profile, and reduced cost formulate them the ideal choice for communication networks. However, the difficulties in structure size and design remain a major concern. Hence, this paper aims to propose a new model that derives a nonlinear objective model for helping in the design of solution space of antenna parameters. To attain this, it is planned to incorporate the optimization concept, thereby a new mutation probability based lion algorithm (MP-LA), which is proposed for the tuning MPA constraints. The main objective model of the antenna design is to maximize the gain by optimizing the patch length, width, thickness of substrate, and value of dielectric substrate. After executing the simulation model, this paper compares the performance of proposed MP-LA-based antenna design with numerous conventional approaches namely, antenna design without optimization, artificial bee colony-based AD, genetic-based AD, firefly-based AD, part icle swarm optimization-based AD and grey wolf optimization-based AD, proposed GWO-based AD and lion optimization algorithm-based AD. Moreover, the analysis is done with respect to radiation pattern, E-plane, and H-plane of proposed and conventional antenna designs. Other performance analysis on characteristics impedance, directivity, efficiency and gain of proposed and conventional models is done.