车计算中基于侏儒猫鼬优化算法的资源共享分配方法
Resource-sharing allocation method based on dwarf mongoose optimization algorithm in vehicle computing
刘曦 1刘俊 2吴鸿 1李伟东3
作者信息
- 1. 曲靖师范学院信息工程学院,云南 曲靖 655011
- 2. 云南经济管理学院教育学院,云南 昆明 650106
- 3. 云南大学数学与统计学院,云南 昆明 650500
- 折叠
摘要
在车计算中,拥有强大计算能力和丰富传感设备的智能车为用户提供服务,其中众多的传感设备能为用户提供不限时间、地点的服务.智能车拥有大量计算资源和传感资源,其中计算资源为单个用户独享,而传感资源能被多个用户共享.针对车计算的特点,首先设计了一种基于资源共享的资源分配新模型,提出一种基于侏儒猫鼬优化算法的资源共享分配方法.然后针对资源分配的离散问题,提出一种不可行解的修正算法.最后为了解决侏儒猫鼬优化算法易于陷入局部最优解的问题,提出一种基于随机和贪心策略结合的初始解生成算法,以提高算法收敛速度,使其能够快速得到最优分配方案.实验结果表明,所提方法在不同的分配环境下均有较好的表现,并且有较强的适应能力.
Abstract
In vehicle computing,the intelligent vehicle which has strong computing capability and abundant sensing de-vices provides services for users.Many sensing devices can provide services for users without the limits of time and place.Intelligent vehicles have large amount computing and sensing resources,where computing resources are used indi-vidually by users while sensing resources can be shared by multiple users.According to the characteristics of intelligent vehicles,a new resource allocation model based on resource sharing was proposed.A resource sharing allocation method based on dwarf mongoose optimization was proposed.A repairing algorithm was proposed to transform infeasible solu-tions into feasible solutions.A new solution generation algorithm based on the random and greedy strategy was proposed to address the problem of the dwarf mongoose optimization algorithm being prone to getting stuck in local optima,to im-prove the convergence speed and obtain the optimal solution.The experimental results show that the proposed strategy performs well in different allocation environments and is adaptable.
关键词
车计算/侏儒猫鼬优化算法/资源共享/资源分配Key words
vehicle computing/dwarf mongoose optimization algorithm/resource sharing/resource allocation引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据