Many real-world optimization problems possess dynamic characteristics. Evolutionary dynamic optimization algorithms (EDOAs) aim to tackle the challenges associated with dynamic optimization problems. Looking at the existing works, the results reported for a given EDOA can sometimes be considerably different. This issue occurs because the source codes of many EDOAs, which are usually very complex algorithms, have not been made publicly available. Indeed, the complexity of components and mechanisms used in many EDOAs makes their re-implementation error-prone. In this paper, to assist researchers in performing experiments and comparing their algorithms against several EDOAs, we develop an open-source MATLAB platform for EDOAs, called Evolutionary Dynamic Optimization LABoratory (EDOLAB). This platform also contains an education module that can be used for educational purposes. In the education module, the user can observe a) a 2-dimensional problem space and how its morphology changes after each environmental change, b) the behaviors of individuals over time, and c) how the EDOA reacts to environmental changes and tries to track the moving optimum. In addition to being useful for research and education purposes, EDOLAB can also be used by practitioners to solve their real-world problems. The current version of EDOLAB includes 25 EDOAs and three fully-parametric benchmark generators. The MATLAB source code for EDOLAB is publicly available and can be accessed from [https://github.com/EDOLAB-platform/EDOLAB-MATLAB].

翻译：许多现实世界的优化问题具有动态特性。进化动态优化算法（EDOAs）旨在应对动态优化问题带来的挑战。现有研究表明，针对某一特定EDOA所报告的结果有时存在显著差异。这一现象的产生是因为许多EDOA的源代码（通常极为复杂的算法）并未公开。事实上，众多EDOA所用组件与机制的复杂性使得其重新实现容易出错。为帮助研究者开展实验并将自己的算法与多种EDOA进行对比，本文开发了一个开源的MATLAB平台——进化动态优化实验室（EDOLAB）。该平台还包含一个可用于教育目的的教学模块。在教学中，用户可以观察：a)二维问题空间及其每次环境变化后的形态演变，b)个体随时间的行为演化，c)EDOA如何响应环境变化并尝试追踪移动的最优解。除辅助研究与教育外，EDOLAB也可供实践者用于解决现实世界问题。当前版本的EDOLAB包含25种EDOA算法与三个全参数化基准生成器。EDOLAB的MATLAB源代码已公开，可通过[https://github.com/EDOLAB-platform/EDOLAB-MATLAB]访问获取。