进化计算的IEEE期刊TEC(IEEE Transactions on Evolutionary Computation)出版高质量的进化计算和相关领域的原始文献,包括自然启发算法、基于种群的方法、选择和变异不可分割的优化以及混合系统。纯理论论文被认为是提供这些计算领域一般见解的应用论文。本杂志的文章按照IEEE PSPB操作手册(章节8.2.1.C和8.2.2.A)的要求进行同行评审。每一篇发表的文章都由至少两名独立的审稿人通过单盲的同行评审过程进行评审。 官网地址:http://dblp.uni-trier.de/db/journals/tec/

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Edge computing-enhanced Internet of Vehicles (EC-IoV) enables ubiquitous data processing and content sharing among vehicles and terrestrial edge computing (TEC) infrastructures (e.g., 5G base stations and roadside units) with little or no human intervention, plays a key role in the intelligent transportation systems. However, EC-IoV is heavily dependent on the connections and interactions between vehicles and TEC infrastructures, thus will break down in some remote areas where TEC infrastructures are unavailable (e.g., desert, isolated islands and disaster-stricken areas). Driven by the ubiquitous connections and global-area coverage, space-air-ground integrated networks (SAGINs) efficiently support seamless coverage and efficient resource management, represent the next frontier for edge computing. In light of this, we first review the state-of-the-art edge computing research for SAGINs in this article. After discussing several existing orbital and aerial edge computing architectures, we propose a framework of edge computing-enabled space-air-ground integrated networks (EC-SAGINs) to support various IoV services for the vehicles in remote areas. The main objective of the framework is to minimize the task completion time and satellite resource usage. To this end, a pre-classification scheme is presented to reduce the size of action space, and a deep imitation learning (DIL) driven offloading and caching algorithm is proposed to achieve real-time decision making. Simulation results show the effectiveness of our proposed scheme. At last, we also discuss some technology challenges and future directions.

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