Antennas Optimal Design Using Opposition-Based Learning and Altruistic Aquila Optimizer Algorithm

In order to improve the working performance of the monopole dual-band antenna, this paper proposes a hybrid Aquila optimization (HAO) based on opposition-based learning strategy and altruism to optimize the parameters of the monopole antenna. HAO enhances the diversity of the algorithm population through opposition-based learning strategy, improves the algorithm convergence speed, and integrates altruism to improve the algorithm's ability to jump out of the local optimum. The HAO is compared with the unimodal benchmark and Multimodal benchmark test functions, and the test results show that the HAO has better performance. Finally, EAO successfully optimized a dual-band monopole antenna that can work in 2.4/5.2GHz WLAN systems.