Integrated FAS and RIS configuration in next-generation wireless systems via Differentiated Creative Search algorithm

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The integration of Fluid Antenna System (FAS) and Reconfigurable Intelligent Surface (RIS) offers an effective approach to improve wireless communication by tackling issues including signal deterioration, interference, and security. This work presents the Differentiated Creative Search (DCS) algorithm, inspired by human learning and creativity, to optimize an integrated RIS and FAS configurations simultaneously. The proposed DCS employs two fundamental strategies. First, differentiated knowledge acquisition (DKA) adjusts learning rates in response to performance. Second, creative realism encourages diverse solution exploration through Linnik-distribution-based mutations. It dynamically modifies the number and location of RIS units and FAS ports based on environmental conditions and communication demands. To maximize system performance, three objectives are considered: (i) minimizing RIS elements while maximizing user achievable rate, (ii) minimizing both RIS elements and active FAS ports per user, and (iii) weighted combined optimization of all performance metrics. Simulation results demonstrate that the DCS algorithm derives the most robust algorithm under the joint optimization objective function, as it achieves 24.7% and 19.7% improvement in mean objective score compared to Blood-Sucking Leech Optimizer (BSLO) and Quadratic Interpolation Optimization (QIO), respectively. Additionally, DCS achieves 52.4%, 53.3% and 71% less variation compared to Black-winged Kite Algorithm (BKA), Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO), respectively.