Multiobjective optimization is a hot topic in the artificial intelligence and operations research communities. The design and development of multiobjective methods is a frequent task for researchers and practitioners. As a result of this vibrant activity, a myriad of techniques have been proposed in the literature to date, demonstrating a significant effectiveness for dealing with situations coming from a wide range of real-world areas. This paper is focused on a multiobjective problem related to optimizing Infrastructure-as-Code deployment configurations. The system implemented for solving this problem has been coined as IaC Optimizer Platform (IOP). Despite the fact that a prototypical version of the IOP has been introduced in the literature before, a deeper analysis focused on the resolution of the problem is needed, in order to determine which is the most appropriate multiobjective method for embedding in the IOP. The main motivation behind the analysis conducted in this work is to enhance the IOP performance as much as possible. This is a crucial aspect of this system, deeming that it will be deployed in a real environment, as it is being developed as part of a H2020 European project. Going deeper, we resort in this paper to nine different evolutionary computation-based multiobjective algorithms. For assessing the quality of the considered solvers, 12 different problem instances have been generated based on real-world settings. Results obtained by each method after 10 independent runs have been compared using Friedman's non-parametric tests. Findings reached from the tests carried out lad to the creation of a multi-algorithm system, capable of applying different techniques according to the user's needs.

翻译：多目标优化是人工智能与运筹学领域的研究热点。多目标方法的设计与开发是研究人员和从业者的常见任务。基于这一活跃的研究活动，迄今为止文献中已涌现出大量技术手段，在应对现实世界中各类复杂场景时展现出显著成效。本文聚焦于基础设施即代码部署配置的优化这一多目标问题，为解决该问题开发的系统被命名为IaC优化平台（IOP）。尽管文献中已提出IOP的原型版本，但需针对问题求解开展更深层分析，以确定最适合嵌入IOP的多目标方法。本研究分析的主旨在于最大限度提升IOP性能，这对该系统至关重要——因其作为H2020欧洲项目的重要组成部分，最终将部署于真实环境。深入研究后，我们采用了九种基于进化计算的多目标算法。为评估各求解器的质量，基于真实场景生成了12个不同的问题实例。通过Friedman非参数检验比较了各方法在10次独立运行后获得的结果。基于检验结果构建出多算法系统，可根据用户需求灵活应用不同技术。