| Issue |
ITM Web Conf.
Volume 59, 2024
II International Workshop “Hybrid Methods of Modeling and Optimization in Complex Systems” (HMMOCS-II 2023)
|
|
|---|---|---|
| Article Number | 02024 | |
| Number of page(s) | 14 | |
| Section | Interdisciplinary Mathematical Modeling and Applications | |
| DOI | https://doi.org/10.1051/itmconf/20245902024 | |
| Published online | 25 January 2024 | |
A comparative study of state-of-the-art multi-objective optimization algorithms
1
iberian Federal University, Institute of Space and Information Technology,
Krasnoyarsk,
Russia
2
eshetnev Siberian State University of Science and Technology, Institute of Informatics and Telecommunications,
Krasnoyarsk,
Russia
* Corresponding author: evgenysopov@gmail.com
With the development of intelligent algorithms, multi-objective optimization problems are increasingly showing a significant role in various fields. In this paper, we used four multi-objective optimization algorithms and tested them on six ZDT standard test problems. Conducted experiments to analyse the optimization effects of the algorithms and determine the strengths and weaknesses of each. These analyses help to identify the most appropriate optimization algorithm for a given problem.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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