标题： 进化算法在雾架构服务放置优化中的评估与效率比较 显示英文标题

标题： Evaluation and Efficiency Comparison of Evolutionary Algorithms for Service Placement Optimization in Fog Architectures

摘要： 本研究比较了三种进化算法在雾服务放置问题中的应用：加权和遗传算法（WSGA）、非支配排序遗传算法II（NSGA-II）和基于分解的多目标进化算法（MOEA/D）。 提出了问题领域的模型（雾架构和雾应用）以及优化模型（目标函数和解决方案）。 我们的主要关注点是优化网络延迟、服务分布和资源使用。 算法通过一个包含100个设备的随机Barabasi-Albert网络拓扑以及两个实验规模的100和200个应用程序服务进行评估。 结果表明， NSGA-II在目标优化和解空间多样性方面取得了最高的优化效果。 相反，MOEA/D在减少执行时间方面表现更好。 WSGA算法在与其他两种算法相比时没有表现出任何优势。 显示英文摘要

摘要： This study compares three evolutionary algorithms for the problem of fog service placement: weighted sum genetic algorithm (WSGA), non-dominated sorting genetic algorithm II (NSGA-II), and multiobjective evolutionary algorithm based on decomposition (MOEA/D). A model for the problem domain (fog architecture and fog applications) and for the optimization (objective functions and solutions) is presented. Our main concerns are related to optimize the network latency, the service spread and the use of the resources. The algorithms are evaluated with a random Barabasi-Albert network topology with 100 devices and with two experiment sizes of 100 and 200 application services. The results showed that NSGA-II obtained the highest optimizations of the objectives and the highest diversity of the solution space. On the contrary, MOEA/D was better to reduce the execution times. The WSGA algorithm did not show any benefit with regard to the other two algorithms.