5G is a future wireless technology that enhances capacity and provides very low latency, very high data rates, and good quality of service. For every 5G device, the design of a nano-antenna plays a very important role to function effectively and it directs to improve efficient communication, channel capacity, and spectrum efficiency. Different approaches have been developed to enhance the antenna performance. However, a compact size, cost-effective, enhanced bandwidth, gain, and negligible radiation losses based antenna design still faces changing tasks. In order to improve the efficiency of nano-antenna design, a novel technique called Elitism Divergence Multi objective Harris Hawks Optimization (EDHOP) technique is introduced. By applying a proposed optimization technique, populations of Harris hawks (i.e. nano-antenna) are initialized. For each Harris Hawk, the fitness is estimated along with the multi-objective functions such as distance, thickness, width, length, and wavelength of an incident of light, gain. The Elitism selection is applied in a Harris hawks optimization to randomly select the nano-antenna with the best fitness. Following, the global optimum solution is identified from the population based on the position updates. The simulation of the proposed EDHOP technique is conducted using a MATLAB simulator with various performance metrics such as heat loss, thermal loss, SWR, electric field, and radiation efficiency. The simulation results demonstrate that the proposed EDHOP technique improves the performance of nano-antenna design with minimum heat loss, thermal loss, SWR, electric field, and higher efficiency with respect to wavelength respectively.

5G wireless communication, Nano-Antenna Design, Jenson Shannon divergence, Elitism selection, Harris hawks optimization