Algorithm 1060: EDOLAB, a Platform for Research and Education in Evolutionary Dynamic Optimization

Peng, M, Yazdani, D, Yazdani, D, She, Z, Luo, W, Li, C, Branke, J, Nguyen, TT ORCID: 0000-0002-3268-1790, Gandomi, AH, Yang, S, Jin, Y and Yao, X (2026) Algorithm 1060: EDOLAB, a Platform for Research and Education in Evolutionary Dynamic Optimization. ACM Transactions on Mathematical Software, 52 (1). ISSN 0098-3500

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Abstract

Many real-world optimization problems exhibit dynamic characteristics, posing significant challenges for traditional optimization methods. Evolutionary Dynamic Optimization Algorithms (EDOAs) have been developed to address these challenges by adapting to changing environments over time. However, the reproducibility and consistency of experimental results in the literature remain limited due to the lack of publicly available source codes and the complexity of accurately re-implementing algorithms and performance evaluation protocols. To support the community, we introduce Evolutionary Dynamic Optimization LABoratory (EDOLAB), an open source MATLAB platform designed for both research and educational purposes. EDOLAB includes 27 EDOAs, four highly configurable benchmark generators, and a growing suite of performance indicators. The platform supports full parameter tuning, batch experiment management, parallel execution, and automated statistical comparisons—including rankings, significance testing, box plots, and performance trend visualizations over time. An educational application allows users to observe: (a) dynamic changes in a 2D problem landscape, (b) the movement of individuals in response to these changes, and (c) the ability of an algorithm to track moving optima. By providing an integrated environment for experimentation, benchmarking, and instructional use, EDOLAB promotes reproducibility, comparative analysis, and a deeper understanding of EDOAs in dynamic environments.

Item Type:	Article
Uncontrolled Keywords:	Dynamic optimization problems; evolutionary dynamic optimization; benchmarking platform; educational tools; algorithm analysis; reproducible research; MATLAB software; 4605 Data Management and Data Science; 46 Information and Computing Sciences; Networking and Information Technology R&D (NITRD); 4 Quality Education; 0802 Computation Theory and Mathematics; 0806 Information Systems; Numerical & Computational Mathematics; 4606 Distributed computing and systems software; 4613 Theory of computation; 4901 Applied mathematics
Subjects:	L Education > LB Theory and practice of education Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Engineering and Built Environment
Publisher:	Association for Computing Machinery
Date of acceptance:	21 November 2025
Date of first compliant Open Access:	28 August 2026
Date Deposited:	28 Aug 2026 15:14
Last Modified:	28 Aug 2026 15:14
DOI or ID number:	10.1145/3785134
URI:	https://researchonline.ljmu.ac.uk/id/eprint/29226

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