Beyond LLM-GA: Secure Fluid Antenna Systems with ReEvo-Designed Memetic Algorithm
Authors: Hanyong Xu, Zhaolai Dang, Tong Zhang
Organizations: School of Information, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, China · Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology, Harbin Institute of Technology, Shenzhen, 518055, China
Fluid antenna systems (FASs) offer significant spatial flexibility, yet securing them against eavesdropping is critical for practical FAS deployment in military, satellite, and internet-of-things networks. Although large language model (LLM)-assisted genetic algorithms (LLM-GAs) can address this secure FAS port selection problem, whether further algorithmic improvement is possible warrants deeper investigation. To this end, we propose a memetic algorithm based on reflective evolution (ReEvo). Unlike the state-of-the-art LLM-GAs, which design only crossover or mutation operators with an LLM, our algorithm leverages an LLM to evolve dedicated crossover, mutation, and local-search operators offline. These operators are then embedded into a memetic search framework, thereby obviating any online LLM queries during execution. Simulation results at equal generation counts demonstrate that our proposed algorithm achieves a higher secure sum-rate than the conventional GA and the state-of-the-art LLM-GAs.
Figures & tables
Operator class
Ref. [ 13 ]
Ref. [ 14 ]
Proposed
(crossover only)
(mutation only)
(memetic)
LLM-Designed Mutation
No
Yes
Yes
LLM-Designed Crossover
Yes
No
Yes
LLM-Designed Local Search
No
No
Yes
TABLE I: Comparison of the LLM-designed operators in [ 13 ] , [ 14 ] , and the proposed algorithm.
Fig. 1: Flowchart comparison of [ 13 ] and [ 14 ] with the proposed algorithm.
Fig. 2: Mean secure sum-rate versus total transmit power.
Fig. 3: Secure sum-rate convergence for five total transmit power levels.
Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden · School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN USA
School of Computer Science and Technology, Xidian University, Xi’an, 710071 China · Department of Applied Data Science, San Jose State University, San Jose, CA, U. S. A.