In the last few years, the formulation of real-world optimization problems and their efficient solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In spite of decades of historical advancements on the design and use of metaheuristics, large difficulties still remain in regards to the understandability, algorithmic design uprightness, and performance verifiability of new technical achievements. A clear example stems from the scarce replicability of works dealing with metaheuristics used for optimization, which is often infeasible due to ambiguity and lack of detail in the presentation of the methods to be reproduced. Additionally, in many cases, there is a questionable statistical significance of their reported results. This work aims at providing the audience with a proposal of good practices which should be embraced when conducting studies about metaheuristics methods used for optimization in order to provide scientific rigor, value and transparency. To this end, we introduce a step by step methodology covering every research phase that should be followed when addressing this scientific field. Specifically, frequently overlooked yet crucial aspects and useful recommendations will be discussed in regards to the formulation of the problem, solution encoding, implementation of search operators, evaluation metrics, design of experiments, and considerations for real-world performance, among others. Finally, we will outline important considerations, challenges, and research directions for the success of newly developed optimization metaheuristics in their deployment and operation over real-world application environments.

翻译：近年来，现实世界优化问题的建模及其通过元启发式算法的高效求解已成为大量研究的催化剂。尽管元启发式算法的设计与应用已有数十年的历史进展，但在新技术的可理解性、算法设计严谨性以及性能可验证性方面仍存在巨大困难。一个明显的例子源于涉及优化用元启发式算法研究的可复现性不足，这往往是由于待复现方法描述中的模糊性和细节缺失所致。此外，在许多情况下，其报告结果的统计显著性也存在疑问。本研究旨在为读者提供一套关于优化用元启发式方法研究时应采纳的良好实践建议，以确保科学严谨性、价值与透明度。为此，我们提出了一套分步研究方法论，涵盖处理该科学领域时应遵循的每个研究阶段。具体而言，将讨论常被忽视却至关重要的方面及实用建议，涉及问题建模、解编码、搜索算子实现、评估指标、实验设计以及现实性能考量等。最后，我们将概述新开发的优化元启发式算法在现实应用环境中部署和运行时需考虑的重要事项、挑战及研究方向。