EKG-NSGA-III: An Expert Knowledge-Guided Improved NSGA-III for Large-Scale Frequency Assignment in Ultra-Dense Heterogeneous Networks

To address the critical requirements for electromagnetic spectrum orchestration in complex ultra-dense communication environments, this paper proposes an Expert Knowledge-Guided improved NSGA-III framework to solve large-scale frequency assignment problems efficiently. which is built upon the standard NSGA-III architecture as the algorithmic backbone. Traditional multi-objective evolutionary algorithms often struggle with slow convergence and insufficient local search capabilities when navigating high-dimensional, strongly constrained search spaces. In this study, we first introduce a Conflict Graph-based Intelligent Initialization strategy to generate high-quality initial populations by constructing an interference conflict graph based on network topology. Second, a Knowledge-Guided Mutation operator is designed to precisely identify and reconfigure conflicting communication nodes using physical layer indicators. Furthermore, a Best-individual Guided Double-Scale Mutation mechanism is incorporated to dynamically balance global exploration and local exploitation. Experimental results on complex multi-node datasets demonstrate that EKG-NSGA-III significantly outperforms the standard NSGA-III and other baseline algorithms in terms of Hypervolume and Inverted Generational Distance. Specifically, for the 200 nodes scenario, the proposed method achieves a 25.7% improvement in IGD and a 4.2% increase in HV compared to the standard NSGA-III. The proposed algorithm provides a robust and efficient solution for spectrum management in complex urban electromagnetic environments, such as future smart city infrastructures.