Multicloud computing is a strategy that helps customers to reduce reliance on any single cloud provider (known as the vendor lock‐in problem). The value of such strategy increases with proper selection of qualified service providers. In this paper, a constrained multicriteria multicloud provider selection mathematical model is proposed. Three metaheuristics algorithms (simulated annealing [SA], genetic algorithm [GA], and particle swarm optimization algorithm [PSO]) were implemented to solve the model, and their performance was studied and compared using a hypothetical case study. For the sake of comparison, Taguchi's robust design method was used to select the algorithms' parameters values, an initial feasible solution was generated using analytic hierarchy process (AHP)—as the most used method to solve the cloud provider selection problem in the literature, all three algorithms used that solution and, in order to avoid AHP limitations, another initial solution was generated randomly and used by the three algorithm in a second set of performance experiments. Results showed that SA, GA, PSO improved the AHP solution by 53.75%, 60.41%, and 60.02%, respectively, SA and PSO are robust because of reaching the same best solution in spite of the initial solution.

中文翻译：

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多云环境中云服务提供商选择的多标准优化模型

多云计算是一种策略，可帮助客户减少对任何单一云提供商的依赖（称为供应商锁定问题）。The value of such strategy increases with proper selection of qualified service providers. 在本文中，提出了一种受约束的多标准多云提供商选择数学模型。实现了三种元启发式算法（模拟退火 [SA]、遗传算法 [GA] 和粒子群优化算法 [PSO]）来求解模型，并使用假设案例研究来研究和比较它们的性能。为了比较，采用田口鲁棒设计方法选择算法的参数值，使用层次分析法 (AHP) 生成初始可行解——作为文献中最常用的解决云提供商选择问题的方法，所有三种算法都使用该解，为了避免 AHP 限制，生成了另一个初始解随机并由三种算法在第二组性能实验中使用。结果表明，SA、GA、PSO 分别将 AHP 解提高了 53.75%、60.41% 和 60.02%，SA 和 PSO 是稳健的，因为尽管初始解达到了相同的最佳解。