改进鲸鱼优化算法的车联网计算卸载
Computating Offloading Based on Improved Whale Optimization Algorithm in IoV
赵振博 1任雪容 1付青坤1
作者信息
- 1. 长安大学信息工程学院,西安 710064
- 折叠
摘要
在边缘服务器资源受限的情况下,如何设计合理的资源管理和任务调度方案是一项重要的研究内容.为提升系统服务效用,提出一种联合资源分配和计算卸载的设计方案.首先,借助二分搜索法和拉格朗日乘子法得到通信和计算资源的最佳匹配.然后,基于融合多种策略的鲸鱼优化算法来求解卸载决策,其中包括调整收敛因子为指数幂级的非线性变化策略,平衡探索和利用阶段的自适应权重策略,三角形和Levy飞行的游走策略,同时在适应度评价中引入罚函数来达到用户接入数量的约束限制,最后利用V型传递函数制定二进制卸载策略.仿真结果表明,在与其他基准方案的多项指标评估中,所提方案能有效增加网络吞吐量,显著提高系统效用.
Abstract
As the resources of edge servers are limited,how to design a reasonable resource management and task scheduling scheme is important research.To improve the utility of system services,this study proposes the strategy of joint resource allocation and computing offloading.Firstly,the optimal matching of communication and computing resources is obtained by binary search and the Lagrange multiplier method.Then,the offloading decision is made based on the whale optimization algorithm integrating with multiple strategies,including adjusting the convergence factor with a nonlinear change strategy of the exponential power,the adaptive weight strategy balancing the exploration and utilization stage,and the wandering strategy of the triangle and Levy flight.Besides,the study introduces a penalty function in fitness evaluation to satisfy the constraint of user access.Finally,it formulates a V-shaped transfer function to make binary offloading decisions.The simulation results show that in various indicator evaluations with other benchmark schemes,the proposed strategy can effectively increase network throughput and significantly improve system utility.
关键词
资源分配/计算卸载/鲸鱼优化算法/自适应权重/罚函数/传递函数/车联网Key words
resource allocation/computing offloading/whale optimization algorithm(WOA)/adaptive weight/penalty function/transfer function/Internet of Vehicle(IoV)引用本文复制引用
出版年
2024
NETL
NSTL
万方数据