AEFA: Artificial Electric Field Algorithm

版本 1.0.0 (27.7 KB) 作者: Dr Anupam Yadav

This simulates the charge movement of AEFA

关注

0.0

(0)

679.0 次下载

更新时间 2019/4/15

查看许可证

Electrostatic Force is one of the fundamental force of physical world. The concept of electric field and charged particles provide us a strong theory for the working force of attraction or repulsion between two charged particles. In the recent years many heuristic optimization algorithms are proposed based on natural phenomenon. The current article proposes a novel artificial electric field algorithm (AEFA) which inspired by the Coulomb's law of electrostatic force. The AEFA has been designed to work as a population based optimization algorithm, the concept of charge is extended to fitness value of the population in an innovative way.

引用格式

Dr Anupam Yadav (2024). AEFA: Artificial Electric Field Algorithm (https://www.mathworks.com/matlabcentral/fileexchange/71218-aefa-artificial-electric-field-algorithm), MATLAB Central File Exchange. 检索时间: 2024/11/22.

Anita, and Anupam Yadav. “AEFA: Artificial Electric Field Algorithm for Global Optimization.” Swarm and Evolutionary Computation, vol. 48, Elsevier BV, Aug. 2019, pp. 93–108, doi:10.1016/j.swevo.2019.03.013.

查看更多格式

MATLAB 版本兼容性

创建方式 R2019a

兼容任何版本

平台兼容性

Windows macOS Linux

类别

Sciences > Material Sciences > Thermal Analysis >

在 Help Center 和 MATLAB Answers 中查找有关 Thermal Analysis 的更多信息

标签添加标签

致谢

参考作品: Gravitational Search Algorithm (GSA)

启发作品: iAEFA, AEFA for Training Neural Network (GAEFA-HK)

Community Treasure Hunt

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

Start Hunting!

版本	已发布	发行说明
1.0.0	2019/4/15		Zip Toolbox

MLA	Anita, and Anupam Yadav. “AEFA: Artificial Electric Field Algorithm for Global Optimization.” Swarm and Evolutionary Computation, vol. 48, Elsevier BV, Aug. 2019, pp. 93–108, doi:10.1016/j.swevo.2019.03.013.
APA	Anita, & Yadav, A. (2019). AEFA: Artificial electric field algorithm for global optimization. Swarm and Evolutionary Computation, 48, 93–108. Elsevier BV. Retrieved from https://doi.org/10.1016%2Fj.swevo.2019.03.013
BibTeX	@article{Anita_2019, doi = {10.1016/j.swevo.2019.03.013}, url = {https://doi.org/10.1016%2Fj.swevo.2019.03.013}, year = 2019, month = {aug}, publisher = {Elsevier {BV}}, volume = {48}, pages = {93--108}, author = {Anita and Anupam Yadav}, title = {{AEFA}: Artificial electric field algorithm for global optimization}, journal = {Swarm and Evolutionary Computation} }