---
title: "Hierarchical Intelligent Evolutionary Optimization for UAV-Assisted NOMA-MEC Systems"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.04474v1"
permalink: "https://feed.astralor.com/items/itm_80c15e12362871f2cdef2a63"
published: "2026-11-03T08:04:55.000Z"
score: 60
channel: "tech"
tags: ["推理效率","Hierarchical Intelligent Evolutionary","Optimization","UAV-Assisted NOMA-MEC Systems","UAVs","MEC","NOMA"]
---

# Hierarchical Intelligent Evolutionary Optimization for UAV-Assisted NOMA-MEC Systems

## 速览

The integration of unmanned aerial vehicles (UAVs), mobile edge computing (MEC) and non-orthogonal multiple access (NOMA) provides an effective solution for computation-intensive and latency-sensitive services in infras…

## 关键点

- The integration of unmanned aerial vehicles (UAVs), mobile edge computing (MEC) and non-orthogonal multiple access (NOMA) provides an effective solution for computation-intensive and latency-sensitive services in infras…
- However, the joint optimization of UAV trajectories, task offloading, communication and computing resources under heterogeneous task priorities results in a highly coupled mixed-integer non-convex problem.
- To address these challenges, this paper proposes a Hierarchical Intelligent Evolutionary Algorithm (HIEA) for priority-aware multi-UAV cooperative NOMA-MEC networks, which integrates multi-head deep Q-network (MH-DQN)-b…

## 全文

The integration of unmanned aerial vehicles (UAVs), mobile edge computing (MEC) and non-orthogonal multiple access (NOMA) provides an effective solution for computation-intensive and latency-sensitive services in infrastructure-deficient environments. However, the joint optimization of UAV trajectories, task offloading, communication and computing resources under heterogeneous task priorities results in a highly coupled mixed-integer non-convex problem. To address these challenges, this paper proposes a Hierarchical Intelligent Evolutionary Algorithm (HIEA) for priority-aware multi-UAV cooperative NOMA-MEC networks, which integrates multi-head deep Q-network (MH-DQN)-based intelligent decision generation, communication-aware initialization, adaptive evolutionary exploration and successive convex approximation (SCA)-based precision refinement. Specifically, a novel communication-aware initialization mechanism, termed Channel Quality Indicator (CQI)-guided Load-Aware Trajectory Initialization (CQI-LATI), is designed to incorporate CQI and task workload characteristics into evolutionary population construction, generating high-quality and diversity-preserving initial populations. Theoretical analysis characterizes HIEA's search-space reduction, the quality-diversity trade-off of CQI-guided initialization, and the accuracy-efficiency trade-off of SCA refinement, and extensive simulations demonstrate that HIEA outperforms benchmark schemes, with gains of up to 6.5% in throughput, 13.6% in task completion rate and 15.3% in energy efficiency.

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