Many real-world optimization problems exhibit dynamic characteristics, posing significant challenges for traditional optimization techniques. Evolutionary Dynamic Optimization Algorithms (EDOAs) are designed to address these challenges effectively. However, in existing literature, the reported results for a given EDOA can vary significantly. This inconsistency often arises because the source codes for many EDOAs, which are typically complex, have not been made publicly available, leading to error-prone re-implementations. To support researchers in conducting experiments and comparing their algorithms with various EDOAs, we have developed an open-source MATLAB platform called the Evolutionary Dynamic Optimization LABoratory (EDOLAB). This platform not only facilitates research but also includes an educational module designed for instructional purposes. The education module allows users to observe: a) a 2-dimensional problem space and its morphological changes following each environmental change, b) the behaviors of individuals over time, and c) how the EDOA responds to environmental changes and tracks the moving optimum. The current version of EDOLAB features 25 EDOAs and four fully parametric benchmark generators. The MATLAB source code for EDOLAB is publicly available and can be accessed from [https://github.com/Danial-Yazdani/EDOLAB-MATLAB].

翻译：许多现实世界中的优化问题具有动态特性，这对传统优化技术提出了重大挑战。进化动态优化算法（EDOAs）旨在有效应对这些挑战。然而，在现有文献中，针对特定EDOA所报告的结果可能存在显著差异。这种不一致性通常源于许多通常较为复杂的EDOA源代码未公开，导致容易出错的重现实现。为了支持研究人员开展实验并将其算法与多种EDOA进行比较，我们开发了一个名为进化动态优化实验室（EDOLAB）的开源MATLAB平台。该平台不仅有助于研究，还包含一个专为教学目的设计的教育模块。教育模块允许用户观察：a) 二维问题空间及其在每次环境变化后的形态变化，b) 个体随时间的行为，以及c) EDOA如何响应环境变化并追踪移动最优解。当前版本的EDOLAB包含25种EDOA和四个完全参数化的基准测试生成器。EDOLAB的MATLAB源代码已公开，可从[https://github.com/Danial-Yazdani/EDOLAB-MATLAB]获取。