DEVELOPMENT AND EVALUATION OF HIGH PERFORMANCE HYBRID OPTIMIZATION MECHANISM FOR MICROSTRIP ANTENNA DESIGN CONFIGURATION

Abstract

An efficient and high degree of resolution must be achieved by integrating optimization solutions into the design of high-performance antennas, which has become a prominent consideration in recent years. To achieve the best results when making decisions, optimization procedures like particle swarm optimization (PSO), ant colony optimization (ACO), and mean variance optimization (MVO) have been widely used. However, these optimization algorithms have a number of drawbacks. For example, the ACO-based solution does not support application complexity, while PSO exhibits a low rate of convergence throughout the course of the process cycles. In this work, a hybrid approach for the design of microstrip antennas has been suggested by integrating PSO and MVO techniques, which greatly improves the outcomes, taking into account the limits of existing optimization processes. The proposed hybrid approach is capable of combining the PSO-MVO with multiple parameters like iteration, upper bond, lower bond, and objective function. Finally, research work evaluates the time consumption and error rate with respect to conventional techniques. Results show an improvement of 61% and 50% in the time consumed by the hybrid PSO-MVO approach as compared to the PSO and MVO approach respectively. The hybrid PSO-MVO achieves an accuracy of 87.15 which is 3% better than individual optimization techniques. Simulation work has been made in a Matlab environment in order to get the best solution using a hybrid optimization technique.