Integrating Reconfigurable Intelligent Surface and Modified Aquila Optimization for Enhancing Wireless Communication Capacity

This article introduces a modified version of the Aquila optimization algorithm (AOA) designed to maximize achievable rates in multiuser wireless communication systems equipped with reconfigurable intelligent surface (RIS). The suggested modified AOA (MAOA) integrates randomized dissimilar responses for extensive exploration, reducing the risk of local optima trapping. An adaptive neighborhood search mechanism is involved to enhance exploitation of the local search space, allowing it to focus on refining promising solutions in proximity to the current best solution. The presented study aims to increase the communication system’s capacity and enable it to support more users simultaneously by determining the optimal number of RISs and their installed positions. Two objective models are proposed, either maximizing the average achievable rates of all participants or maximizing the worst achievable rate for individual users. Testing on two different multiuser wireless communication systems, with 20 and 50 users, demonstrates the effectiveness of the proposed MAOA compared to the AOA and other well-known algorithms, including gray wolf optimization (GWO), jellyfish search optimization algorithm (JFSOA), augmented JFSOA (AJFSOA), particle swarm optimization (PSO), and differential evolution (DE). The designed MAOA outperforms other optimization algorithms by 16%–38% in the maximum value of the minimum achievable rate for 20 users, improves the average achievable rate by 74% and 45% compared to AOA. Additionally, the applied algorithms are compared in maximizing average achievable rates across different SNRs, with MAOA achieving rates approximately 40%–58% higher at low SNR and 9%–13% higher at high SNR, highlighting its robustness and efficiency across varying conditions.