  Intelligent Reflecting Surface (IRS) is envisioned to be a promising green
and cost-effective solution to enhance wireless network performance by smartly
reconfiguring the signal propagation. In this paper, we study an IRS-aided
multiple-input single-output wireless network where a multi-antenna Access
Point (AP) services a single-antenna user assisted by an IRS. The goal is to
maximize the received signal power by jointly optimizing the transmit
beamforming at the AP and the reflection coefficient at the IRS. The formulated
optimization problem is non-convex and subject to constraints. We adopt a novel
nature-inspired optimization technique named Harris Hawks Optimizer (HHO) to
tackle the problem. After transforming the constrained problem into an
unconstrained problem using penalty method, the formulated problem is optimized
by the HHO. To the best of our knowledge, this is the first time to use a
meta-heuristic algorithm to solve the IRS-aided network optimization problem.
Simulation is conducted to verify the feasibility of the HHO-based scheme. The
results show that the HHO-based scheme could provide similar or even better
optimization results compared with other optimization algorithms.
