System parameters

This paper introduces an Improved Rime Optimization Algorithm (IROA) designed to maximize achievable rates in multiuser wireless communication networks equipped with Reconfigurable intelligent surfaces (RISs). The proposed technique incorporates the Quadratic Interpolation Method (QIM) into the classic Rime Optimization Algorithm (ROA), which improves solution diversity, facilitates broader exploration of the search space, and enhances robustness against local optima. Finding the ideal quantity and positioning of RIS components to optimize system performance is the main goal of the optimization framework. Two objective models are taken into consideration: one that maximizes the lowest achievable rate in order to prioritize fairness, and another that maximizes the average achievable rate for all users. The performance of IROA is evaluated on systems with 20 and 50 users and compared against established algorithms such as Differential Evolution (DE), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Augmented Jellyfish Search Optimization Algorithm (AJFSOA), and Jellyfish Search Optimization Algorithm (JFSOA). Results demonstrate that the proposed IROA achieves relative performance improvements ranging from 5% to 46% across different scenarios and objective models. In the 20-user case with the first objective model, IROA achieves improvements of 28.02%, 42.07%, 46.54%, 1.74%, 35.46%, and 25.95% compared to AJFSOA, JFSOA, PSO, ROA, GWO, and DE, respectively, in terms of average achievable rate. Similarly, for the second objective model, IROA achieves relative improvements of 5.94%, 13.29%, 14.55%, 7.1%, 15.97%, and 46.26% over ROA, DE, PSO, AJFSOA, JFSOA, and GWO, respectively, in terms of minimum achievable rate. On contrary, the IROA shows lower standard deviation compared to the current ROA. However, the proposed IROA achieves superior performance over ROA in terms of the best, mean and worst objective outcomes. These findings demonstrate that in RIS-assisted wireless communication networks, the suggested IROA achieves strong flexibility and reliable performance benefits across a range of multiuser optimization tasks.