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.

通过比较九种基于进化计算的多目标算法的结果，使用 Friedman 的非参数测试，本文创建了一个多算法系统，根据用户需求能够应用不同的技术，以优化和改进基础设施即代码部署配置的多目标问题。

基于多目标优化的基础设施即代码部署配置分析

Multiobjective Optimization Analysis for Finding Infrastructure-as-Code  Deployment Configurations

In the last years, one of the fields of artificial intelligence that has been
investigated the most is nature-inspired computing. The research done on this
specific topic showcases the interest that sparks in researchers and
practitioners, who put their focus on this paradigm because of the adaptability
and ability of nature-inspired algorithms to reach high-quality outcomes on a
wide range of problems. In fact, this kind of methods has been successfully
applied to solve real-world problems in heterogeneous fields such as medicine,
transportation, industry, or software engineering. Our main objective with this
paper is to describe a tool based on nature-inspired computing for solving a
specific software engineering problem. The problem faced consists of optimizing
Infrastructure as Code deployment configurations. For this reason, the name of
the system is IaC Optimizer Platform. A prototypical version of the IOP was
described in previous works, in which the functionality of this platform was
introduced. With this paper, we take a step forward by describing the final
release of the IOP, highlighting its main contribution regarding the current
state-of-the-art, and justifying the decisions made on its implementation.
Also, we contextualize the IOP within the complete platform in which it is
embedded, describing how a user can benefit from its use. To do that, we also
present and solve a real-world use case.

基于自然启发计算的工具用于解决软件工程问题，介绍了 IaC Optimizer 平台的最终版本，其主要贡献以及实现决策，并展示了一个真实世界的用例。