计算机工程2026，Vol.52Issue(5)：117-128,12.DOI:10.19678/j.issn.1000-3428.0070326
基于改进DQN的最优联盟结构生成策略优化
Optimization of Optimal Coalition Structure Generation Strategy Based on Improved DQN
摘要
Abstract
Edge servers often need to collaborate to execute tasks by forming alliances when resources are limited.Ensuring that tasks can be completed as quickly as possible while reducing the cost of restructuring alliances is a major challenge considering the dynamic changes in server resource utilization for task execution.A coalition structure optimization strategy based on dual Deep Q-Network(DDQN)optimization is proposed to address these issues.First,with the optimization objective of maximizing task completion efficiency and minimizing alliance-building costs,the problem is modeled as a Cost Introduced Markov Decision Process(CT-MDP)by defining the state space,action space,and reward function.Second,in response to the problem of overestimating Q-values in high-dimensional state spaces in the CT-MDP,a lightweight optimal alliance structure search algorithm based on DDQN is proposed.Two independent Q-networks are used to reduce the forward cumulative error during the update process.To satisfy the strict requirements of the edge devices for resource utilization during training,the activation function is optimized to reduce the storage resource requirements of the training model.Finally,the proposed algorithm is compared with Q-learning,DQN,Dueling DQN,and other algorithms using simulation experiments.The results show that the proposed method has good convergence and stability,and it reduces alliance construction costs and resource utilization by 20.36％and 12.12％,respectively,demonstrating the effectiveness of the method.关键词
人工智能/移动边缘计算/计算资源受限/资源调度/联盟结构生成/深度强化学习Key words
Artificial Intelligence(AI)/Mobile Edge Computing(MEC)/limited computing resources/resource scheduling/coalition structure generation/Deep Reinforcement Learning(DRL)分类
信息技术与安全科学引用本文复制引用
赵庶旭,周宏泽,王小龙..基于改进DQN的最优联盟结构生成策略优化[J].计算机工程,2026,52(5):117-128,12.基金项目
甘肃省重点研发计划(20YF8GA123). （20YF8GA123）
