The electromagnetic spectrum serves as a critical resource in advanced fields such as wireless communications, radar detection, and electronic warfare. The Frequency Assignment Problem (FAP) represents a complex optimization issue, encompassing the rational allocation of limited frequency resources to minimize electromagnetic interference (EMI), characterized by multiple objectives, intricate constraints, and dynamic characteristics. Traditionally, FAP is often perceived to a static dimension problem with one-to-one correspondence between equipment and frequencies. However, introducing flexible frequency handoff in the time-domain, it evolves into a one-to-many uncertain relationship, resulting in dynamic variable dimensions of FAP. To address the challenges, this paper create a cross-swarm dimension mutation strategy to generate heterogeneous multi-swarms with diverse variable structures. The life-cycle is set for mutated swarms to implement elimination mechanism, actualizing dynamic adjustment for quantity of swarms. A Dynamic Heterogeneous Multi-Swarm PSO (DHMSPSO) is proposed, which provides a new perspective for solving optimization problems with dynamic variable dimension, and be applied to develop multi-objective frequency assignment algorithm. Ultimately, through simulation experiments on FAP, comparative analysis and statistical tests between DHMSPSO and other optimization algorithms validate the feasibility and superiority of DHMSPSO. And futher simulation for elimination mechanism demonstrates that can prevent excessive swarm expansion and improve the optimization efficiency.

Aerial Radio Access Networks (ARANs) are used to connect aerial nodes (such as satellites, aircraft, floating balloons) and ground infrastructures, which enables a global network coverage and provides a wide range of high-quality network services. At present, extensive researches are to integrate it with Mobile Edge Computing (MEC), to achieve more efficient data computing, data storage, and cache. In this paper, we primarily focus on exploring the edge computing architecture integrated with ARANs. Since the existing MEC architecture is not deeply integrated with ARANs, we propose the scenario of a complete four-tier MEC architecture that allows MECs and ARANs to collaborate effectively. Besides, for environmental protection and cost reduction, we propose a Q-learning algorithm based on the improved ϵ – greedy model to complete the MEC server selection and resource allocation. Finally, the simulation results are compared with other benchmark methods, and the effectiveness of the proposed method is proved. The energy consumption of the proposed method is significantly reduced.