A Novel Archimedes Optimization Algorithm with Levy Flight for Designing Microstrip Patch Antenna

The Archimedes optimization algorithm (AOA) based on the principle of upward force on an object, partially or completely submerged in a liquid, in proportion to the weight of the dispersed liquid has been introduced. It is most famous for its efficiency, simplicity and robustness, but at the same time, it faces problems of premature and slow convergence, due to which it gets trapped in local minima. To overcome these shortcomings, the levy flight has been merged with AOA under this work, hence named as Levy Flight Archimedes optimizer (LAO). Here, the levy flight phase has been used for random walk determining step size. Levy flight plays an important role for improving the exploration phase and for ignoring the local optima of the AOA algorithm during the search process. In this proposed methodology, the limit of each variable is fixed for all decision variables, and if the variable could not mend its own optima solution in the search space at the end of present generation, such limit is improved. If the decision variable crosses the limit value, the levy flight phase helps the decision variable for controlling the speed. The robustness and efficiency of the evolutionary methods have been examined on well-known 29-CEC 2017 test functions and also compared with recent evolutionary algorithms. Furthermore, it has also been successfully applied on four different antenna issues. The metaheuristics MATLAB-R2018a codes have been linked with the two different simulators such as computer simulation tool and ADS (Keysight advanced design system software) to simulate the antennas. Simulated results of LAO method and other metaheuristics have been compared with respect to gain, return loss, directivity, efficiency, fitness score, bandwidth, VSWR, reflection coefficient to prove the robustness of the proposed method.