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A unified evolutionary framework for structural-topological mixed-variable optimization in STAR-RIS-aided systems
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DOI：10.1016/j.swevo.2026.102501.png)
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Abstract
En 中文
Beamforming design in simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided communications exhibits the characteristics of structured-topological mixed-variable optimization (STMVO), involving bounded, discrete, and periodic variables with structured coupling constraints. A theoretical analysis of these structural properties reveals that existing Euclidean-update-based evolutionary baselines may be insufficient when these structural and topological properties are not explicitly considered. From a system-level perspective, these challenges arise from mismatches between optimization dynamics and variable structure, rather than from specific algorithms. Motivated by these insights, we propose ST-EF, a unified evolutionary framework for STMVO. ST-EF introduces three major innovations: (i) a unified representation that embeds heterogeneous variables into a normalized space while preserving topology-specific adjacency; (ii) topology-aware repair operators that respect boundary and periodic conditions, ensuring unbiased geometric consistency; and (iii) topology-aligned deviation mapping with margin correction that maintains balanced sampling over discrete variables and structured coupling constraints, thereby preventing premature collapse. Extensive experiments on STAR-RIS beamforming tasks demonstrate that ST-EF achieves improved robustness and competitive runtime scalability over representative baselines, highlighting its potential as a principled paradigm for solving structured mixed-variable problems.
Keywords:
Simultaneous transmitting and reflecting
reconfigurable intelligent surfaces (STAR-RISs)
Beamforming
Covariance matrix adaptation evolution
strategy
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