With the surge of transportation data and diversification of services, the resources for data processing in intelligent transportation systems become more limited. In order to solve this problem, this paper studies the problem of computation offloading and resource allocation adopting edge computing, NOMA communication technology and edge(content) caching technology in intelligent transportation systems. The goal is to minimize the time consumption and energy consumption of the system for processing structured tasks of terminal devices by jointly optimizing the offloading decisions, caching strategies, computation resource allocation and transmission power allocation. This problem is a mixed integer nonlinear programming problem that is nonconvex. In order to solve this challenging problem, proposed a multi-task multi-objective optimization algorithm (MO-MFEA-S) with adaptive knowledge migration based on MO-MFEA. The results of a large number of simulation experiments demonstrate the convergence and effectiveness of MO-MFEA-S.

中文翻译：

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智能交通场景下基于 MO-MFEA 算法的任务卸载与资源分配联合优化方案


随着交通数据的激增和服务的多样化，智能交通系统中的数据处理资源变得更加有限。为了解决这一问题，本文研究了智能交通系统中采用边缘计算、NOMA通信技术和边缘（内容）缓存技术进行计算卸载和资源分配的问题。目标是通过共同优化卸载决策、缓存策略、计算资源分配和传输功率分配，最大限度地减少系统处理终端设备结构化任务的时间消耗和能耗。此问题是非凸的混合整数非线性规划问题。为了解决这一具有挑战性的问题，提出了一种基于 MO-MFEA 的具有自适应知识迁移的多任务多目标优化算法 （MO-MFEA-S）。大量仿真实验的结果证明了 MO-MFEA-S 的收敛性和有效性。