Improved Artificial Rabbits Algorithm for Positioning Optimization and Energy Control in RIS Multiuser Wireless Communication Systems

Abstract

An innovative method to raise wireless communication systems’ efficiency is to use Reconfigurable Intelligent Surface (RIS). Unfortunately, determining the quantity and locations of the RIS elements continues to be difficult, requiring a clever optimization framework. Concerning the practical overlap between the related multi-RISs in wireless communication systems, this paper attempts to minimize the number of RISs while considering the average possible data rate and technological constraints. In this regard, a novel Enhanced Artificial Rabbits Algorithm (EARA) is developed to minimize the number of RISs to be installed. The novel EARA is inspired by the natural survival strategies of rabbits, including detour eating and random concealment. A more effective method of exploring the search space around the best solution so far is produced by the suggested EARA by combining an upgraded Collaborative Searching Operator (CSO) arrangement. Also, an adaptive time function is included to increase the effect of this exploitation tactic by the increasing number of iterations. The simulation results show that the suggested EARA is highly efficient in reaching the maximum success rate of producing the smallest number of RISs under various feasible rate threshold settings. When EARA is compared to standard Artificial Rabbits Optimizer (ARO), Growth Optimizer (GO), Artificial Ecosystem Optimizer (AEO), and Particle Swarm Optimization (PSO), the average number of RISs is improved by 5.32%, 6.7%, 16.73%, and 20.06%, respectively. Furthermore, according to simulation data, the EARA outperforms AEO, GO, ARO, and PSO in terms of success rate at δ=1.4 by 6.66%, 6.66%, 45.43%, and 99%, respectively.