The Educational Competition Optimizer

版本 1.0.4 (3.1 MB) 作者: Ali Asghar Heidari

This study proposes Educational Competition Optimizer (ECO), created for any optimization case. See: https://aliasgharheidari.com/ECO.html

https://aliasgharheidari.com/ECO.html

关注

0.0

(0)

108.0 次下载

更新时间 2024/10/4

查看许可证

Abstract: In recent research, metaheuristic strategies stand out as powerful tools for complex optimization, capturing widespread attention. This study proposes the Educational Competition Optimizer (ECO), an algorithm created for diverse optimization tasks. ECO draws inspiration from the competitive dynamics observed in real-world educational resource allocation scenarios, harnessing this principle to refine its search process. To further boost its efficiency, the algorithm divides the iterative process into three distinct phases: elementary, middle, and high school. Through this stepwise approach, ECO gradually narrows down the pool of potential solutions, mirroring the gradual competition witnessed within educational systems. This strategic approach ensures a smooth and resourceful transition between ECO's exploration and exploitation phases. The results indicate that ECO attains its peak optimization performance when configured with a population size of 40. Notably, the algorithm's optimization efficacy does not exhibit a strictly linear correlation with population size. To comprehensively evaluate ECO's effectiveness and convergence characteristics, we conducted a rigorous comparative analysis, comparing ECO against nine state-of-the-art metaheuristic algorithms. ECO's remarkable success in efficiently addressing complex optimization problems underscores its potential applicability across diverse real-world domains. The additional resources and open-source code for the proposed ECO can be accessed at https://aliasgharheidari.com/ECO.html

引用格式

Lian, Junbo, et al. “The Educational Competition Optimizer.” International Journal of Systems Science, Informa UK Limited, July 2024, pp. 1–38, doi:10.1080/00207721.2024.2367079.

查看更多格式

MATLAB 版本兼容性

创建方式 R2024a

兼容任何版本

平台兼容性

Windows macOS Linux

标签添加标签

致谢

启发作品: Application of Educational Competition Optimizer in BP model, Application of Educational Competition Optimizer in LSTM, Application of Educational Competition Optimizer in SVM

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Artemisinin Optimizer (AO)-2024

Educational Competition Optimizer (ECO)-2024

Fata Morgana Algorithm (FATA)-2024

Harris Hawk Optimization (HHO)-2019

Hunger Games Search (HGS)-2021

Moss Growth Optimization (MGO)-2024

Parrot Optimizer (PO)-2024

Polar Lights Optimizer (PLO)-2024

Rime Optimization Algorithm (RIME)-2023/RIME Iteration version

Rime Optimization Algorithm (RIME)-2023/RIME function evaluation version

Runge Kutta Optimization (RUN)-2021

Slime mould algorithm (SMA)-2020

Weighted Mean of Vectors (INFO)-2022

版本	已发布	发行说明
1.0.4	2024/10/4	2024	下载
1.0.3	2024/8/9	.	下载
1.0.2	2024/6/15	version 1	下载
1.0.0	2024/6/15		下载

MLA	Lian, Junbo, et al. “The Educational Competition Optimizer.” International Journal of Systems Science, Informa UK Limited, July 2024, pp. 1–38, doi:10.1080/00207721.2024.2367079.
APA	Lian, J., Zhu, T., Ma, L., Wu, X., Heidari, A. A., Chen, Y., Chen, H., et al. (2024). The educational competition optimizer. International Journal of Systems Science, 1–38. Informa UK Limited. Retrieved from http://dx.doi.org/10.1080/00207721.2024.2367079
BibTeX	@article{Lian_2024, title={The educational competition optimizer}, ISSN={1464-5319}, url={http://dx.doi.org/10.1080/00207721.2024.2367079}, DOI={10.1080/00207721.2024.2367079}, journal={International Journal of Systems Science}, publisher={Informa UK Limited}, author={Lian, Junbo and Zhu, Ting and Ma, Ling and Wu, Xincan and Heidari, Ali Asghar and Chen, Yi and Chen, Huiling and Hui, Guohua}, year={2024}, month=jul, pages={1–38} }