Dynamic and Adaptive GA-based Optimization of Active Reconfigurable Intelligent Surface Assisted MU-MISO Networks

Abstract This paper investigates spectral efficiency enhancement in active reconfigurable intelligent surface (RIS)assisted multi-user multiple-input single-output (MU-MISO) systems. Unlike conventional passive RIS, active RIS can mitigate the multiplicative fading effect by employing reflectiontype amplification, at the cost of increased optimization complexity and strict power constraints. To address the resulting non-convex joint optimization problem, a genetic algorithm (GA)-based framework is proposed for the simultaneous design of base station precoding and active RIS configuration. Two constraint-handling strategies are developed, namely DynamicGA-RIS and Adaptive-GA-RIS, which regulate power constraints through generation-dependent and feasibility-driven penalty mechanisms, respectively. Simulation results demonstrate that the proposed GA-based approaches outperform baseline systems without RIS assistance. In particular, the adaptive strategy achieves higher spectral efficiency, while the dynamic strategy provides faster convergence and improved power regulation. These results confirm the effectiveness of evolutionary optimization techniques for enhancing the performance of active RIS-assisted wireless networks.