EDOLAB: An Open-Source Platform for Education and Experimentation with Evolutionary Dynamic Optimization Algorithms
Mai Peng, Delaram Yazdani, Zeneng She, Danial Yazdani, Wenjian Luo,, Changhe Li, Juergen Branke, Trung Thanh Nguyen, Amir H. Gandomi, Shengxiang, Yang, Yaochu Jin, Xin Yao

TL;DR
EDOLAB is an open-source MATLAB platform designed to facilitate research and education in evolutionary dynamic optimization algorithms, providing a standardized environment with multiple algorithms and educational tools.
Contribution
The paper introduces EDOLAB, a comprehensive open-source platform with 25 EDOAs and educational modules, addressing reproducibility and learning challenges in dynamic optimization research.
Findings
Includes 25 EDOAs and 4 benchmark generators
Supports research and educational activities
Enhances reproducibility of dynamic optimization experiments
Abstract
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…
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Evolutionary Algorithms and Applications · Advanced Multi-Objective Optimization Algorithms
