Alsaggaf Wafaa, Gafar Mona, Sarhan Shahenda, Shaheen Abdullah M, Alwakeel Ahmed S
Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Department of Computer Engineering and Information, College of Engineering, Wadi Ad Dwaser, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
PLoS One. 2025 Jun 2;20(6):e0323138. doi: 10.1371/journal.pone.0323138. eCollection 2025.
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.
本文介绍了一种改进的黎曼优化算法（IROA），旨在使配备可重构智能表面（RIS）的多用户无线通信网络中的可实现速率最大化。所提出的技术将二次插值法（QIM）纳入经典的黎曼优化算法（ROA），这提高了解的多样性，便于更广泛地探索搜索空间，并增强了对局部最优的鲁棒性。优化框架的主要目标是找到RIS组件的理想数量和位置，以优化系统性能。考虑了两个目标模型：一个是最大化最低可实现速率以优先考虑公平性，另一个是最大化所有用户的平均可实现速率。在具有20个和50个用户的系统上评估了IROA的性能，并与差分进化（DE）、粒子群优化（PSO）、灰狼优化器（GWO）、增强型水母搜索优化算法（AJFSOA）和水母搜索优化算法（JFSOA）等既定算法进行了比较。结果表明，所提出的IROA在不同场景和目标模型下实现了5%至46%的相对性能提升。在具有第一个目标模型的20用户情况下，就平均可实现速率而言，与AJFSOA、JFSOA、PSO、ROA、GWO和DE相比，IROA分别实现了28.02%、42.07%、46.54%、1.74%、35.46%和25.95%的提升。同样，对于第二个目标模型，就最低可实现速率而言，与ROA、DE、PSO、AJFSOA、JFSOA和GWO相比，IROA分别实现了5.94%、13.29%、14.55%、7.1%、15.97%和46.26%的相对提升。相反，与当前的ROA相比，IROA的标准差更低。然而，所提出的IROA在最佳、平均和最差目标结果方面比ROA具有更优的性能。这些发现表明，在RIS辅助的无线通信网络中，所建议的IROA在一系列多用户优化任务中实现了强大的灵活性和可靠的性能优势。
