Implemantation of firefly algorithm on Arduino Uno

Denda Dewatama, Oktriza Melfazen, Mila Fauziyah

Abstract


Not only getting the optimal solution of a problem, embedding the algorithm on the microcontroller is also expected to work optimally without burdening the system and fast response. Getting a microcontroller specification that matches the complexity of an algorithm is necessary so that the system can execute the algorithm perfectly. Values for the basic parameters of optimization algorithms inspired by nature such as the firefly algorithm (FFA) which are interpreted into variables greatly affect the performance of the microcontroller in obtaining the expected optimal solution. The observed performance of the Arduino Uno microcontroller in running the FFA includes execution time and memory capacity required to obtain optimal values based on changes in absorption coefficient, random parameters, iterations, and population. Changes in the absorption coefficient and random parameters affect the optimal value but do not significantly affect the execution time and memory capacity of Arduino Uno. Iteration changes greatly affect execution time and population changes most affect the performance of Arduino Uno. With a dynamic memory capacity of 2 Kb, the FFA can be run with a maximum range of 50 populations and up to 20 iterations.

Keywords


Arduino Uno; Dynamic memory; Firefly algorithm; Iteration; Population

Full Text:

PDF

References


Y. Xin-She., "Multiobjective Firefly Algorithm for Continuous Optimization.," Springer, 2012.

Taheri, S., Mammadov, M.A., Seifollahi, S., "Optimization," A Journal of Mathematical Programming and Operations Research, 2012.

Saharia, BJ., Brahma, H., Sarmah, N., "A Review of Algorithms for Control and Optimization for Energy Management of Hybrid Renewable Energy Systems.," Journal of Renewable and Sustainable Energy, pp. 1-48, 2018.

Tan, K.C., Feng, L., & Jiang, M., "Evolutionary Transfer Optimization - A New Frontier in Evolutionary Computation Research.," IEEE Computational Intelligence Magazine, 16 (1), pp. 22-33, 2021.

X. Yang., "Firefly Algorithm in Optimization Algorithms," Comput Optimization, Methods and Algorithms, pp. 13-31, 2011.

Betul Aygun, Banugunel Kilic, Nursalarici. Ahmetcosar, Bedriye T., "Application of Binary PSO for Public Cloud Resource Allocation System of Video on Demand (VoD) Services," Applied Soft Computing volume 99, February 2021.

Liao T., Socha K., Montes de Oca MA., Stutzle T., Dorigo M., "Ant Colony Optimization for Mixed-variable Optimization Problems," IEEE Trans Evol Comput : 18(4), p. 503, 2013.

Zhang C., Zheng J., Zhou Y., "Two Modified Artificial Bee Colony Algorithms Inspired by Grenade Explosion Method," Neurocomputing Journal 151, pp. 1198-1207, 2015.

Lin J., Ji H., Lin Q., Li Y., "A Bat Optimization Algorithm with Moderate Orientation and Peturbation of Trend," Journal of Algorithms and Computational Technology, p. 15, 2021.

Melfazen, O., Dewatama, D. , "Firefly Algorihm for Optimizing Single-Axis Solar Tracker," Kinetik Game Technology, Information System, Computer Network, Computing, Electronics and Control Vol 6, No. 4, pp. 313-320, November 2021.

Cheung, N.J., Ding, X.M., & Shen, HB., "A Non-homogeneous Firewfly Algorithm and Its Convergence Analysis," Journal Optime Theory Appl 170, pp. 616-628, 2016.

Johari, Nur & Zain, Azlan & Mustaffa, Noorfa & Udin, Amirmudin, "Firefly Algorithm for Optimization Problem," Applied Mechanics and Materials, p. 421, 2013.

Dewatama, D., Melfazen, O., "MPPT Using Firefly Algorithm for Cuk Converter in Photovoltaic," ELKHA : Jurnal Teknik Elektro, Vol.14, No. 1, pp. 34-39, 2021.

Ming-Liang Gao, Xiao-Hai He, Dai-Sheng Lao, Jun Jiang, Qi-Zhi Teng, "Object Tracking Using Firefly Algorithm," ET Computer Vision, pp. 6-10, 2013.

Mahmood, RZ., Al-Jawaaherry, MA., "Firefly Algorithm Implementation Based on Arduino Microcontroller," International Journal of Computer Science and Information Security (IJCSIS) ol. 15, no. 12, pp. 1-5, December 2017.

B. Setiawan, "Study of LoRa (Long Range) Communication for Monitoring of a Ship Electrical System," J. :Phys : Conf Ser 1402044022, 2019.

K. K. Khaing, "Automatic Temperature Control System Using Arduino," in Proceedings of the Third International Conference on Computational Intelligence and Informatics Volume 1090, 2020.

B. Y.A, "The Working Principle of An Arduino," 11th International Conference on Electronics, Computer and Computation (ICECCO), 2014.

Atmel, "Arduino Uno 3 Datasheet," Atmel Product Reference Manual SKU, 2021.

S. Wang, "Solving Two-Dimensional HP Model by Firefly Algorithm and Simplified Energy Function," Mathematical Problems in Engineering, pp. 1-5, 2013.

X. Yang, "Firefly Algorithm," Optimization Algorithms, Comput Optimization, Methods and Algorithms, pp. 13-31, 2011.

Arora S., Singh S., "The Firefly Optimization Algorithm: Convergence Analysis and Parameter Selection," International Journal of Computer Applications, Vol 1 no. 3, pp. 48-52, 2013.

Zhang L., Liu L., Yang X-S, Dai Y., "A Novel Hybrid Firefly Algorithm for Global Optimization," PLoS ONE, vol 11, p. 421, 2016.

J. N. Lina, "Firefly Algorithm for Optimizing Problem," Applied Mechanics and Materials, vol 421, pp. 512-517, September 2013.

S. Shoubao, Qingping, L., & H. L. Yuab S., "A Novel Wise Step Strategy for Firefly Algorithm.," Tandfonline, pp. 1-7, 2014.

M.K.A. Ariyaratne, T.G.I. Fernando, "A Comparative Study on Nature Inspired Algorithms with Firefly Algorithm," International Journal of Engineering and Technology vol 4, pp. 1-7, 2014.




DOI: https://doi.org/10.11591/eei.v12i6.5362

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Bulletin of EEI Stats

Bulletin of Electrical Engineering and Informatics (BEEI)
ISSN: 2089-3191, e-ISSN: 2302-9285
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).