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Predicting Fire Outbreak Caused by Electrical Faults using Artificial Bee Colony Algorithm

by Nuka D. Nwiabu, Okpu E. Okpomo
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 14
Year of Publication: 2018
Authors: Nuka D. Nwiabu, Okpu E. Okpomo
10.5120/ijca2018916197

Nuka D. Nwiabu, Okpu E. Okpomo . Predicting Fire Outbreak Caused by Electrical Faults using Artificial Bee Colony Algorithm. International Journal of Computer Applications. 179, 14 ( Jan 2018), 9-16. DOI=10.5120/ijca2018916197

@article{ 10.5120/ijca2018916197,
author = { Nuka D. Nwiabu, Okpu E. Okpomo },
title = { Predicting Fire Outbreak Caused by Electrical Faults using Artificial Bee Colony Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2018 },
volume = { 179 },
number = { 14 },
month = { Jan },
year = { 2018 },
issn = { 0975-8887 },
pages = { 9-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number14/28866-2018916197/ },
doi = { 10.5120/ijca2018916197 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:55:20.092487+05:30
%A Nuka D. Nwiabu
%A Okpu E. Okpomo
%T Predicting Fire Outbreak Caused by Electrical Faults using Artificial Bee Colony Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 14
%P 9-16
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In Nigeria, there have been increases in damages due to fire outbreaks particularly in industrial and busy environments. Fire outbreak has caused serious injuries to people, loss of lives, damage of properties etc. Methods usually used in predicting fire outbreaks are fire alarm, flame detection, smoke detection algorithm, real-time fire, flame detection etc. This Research work introduces an artificial bee colony heuristic for predicting fire outbreaks in industrial environment in Nigeria. The artificial bee colony heuristic is a swarm-based heuristic, which mimics the foraging behavior of a honey bee swarm. In this paper, artificial bee colony technique was used for predicting fire outbreaks caused by electrical faults. Two Experiments were conducted, the first Experiment (Exp. 1) using 26 different test simulations was performed using different fault resistance, a constant colony size of 20 (area of search) and max Cycles of 5 (maximum number of iteration). It shows that when the fault resistance is between 0.3 ohms - 0.0 ohms, there will be likelihood of danger occurring among all faults at the same time, and none of the faults will be normal. While the second Experiment (Exp. 2) conducted, using 26 different test simulations was performed using different fault resistance, a constant colony size of 100 (area of search) and max Cycles of 50 (maximum number of iteration), it proves that when the fault resistance is between 0.4 ohms - 0.0 ohms, there will be likelihood of danger occurring among all faults at the same time. The results also prove good performance of the predictive ABC system for average convergence at 2.25 at 26 trials and its unique capability to make multiple predictions. The system was simulated and modeled using Matlab 7.5.0(R2007b) program.

References
  1. Karaboga, D. 2005. An idea based on honey bee swarm for numerical optimization. Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department.
  2. Karaboga, D. and Basturk, B. 2007. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39:459–471.
  3. Basu, B. and Mahanti, G. K. 2011. Fire Fly and Artificial Bees Colony Algorithm for Synthesis of Scanned and Broad Side Linear Array Antenna. Progress in Electromagnetics Research B, Vol. 32, 169-190.
  4. Bolaji, A. L., Khader A. T., Al-Betar, M. A., and Awadallah, M. A. 2013. Artificial Bee Colony Algorithm, Its Variants and Applications: A Survey. Journal of Theoretical and Applied Information Technology 20th January. Vol. 47, No.2.
  5. Rahsepar, M., and Mahmoodi, H. 2014. Predicting weekly discharge using Artificial Neural Network (Ann) Optimized by Artificial Bee Colony (ABC) Algorithm: A Case Study. Civil Engineering and Urban Planning International Journal (CiVEJ) Vol.1, No.1, June Edition.
  6. Yassine, A., Khalid, J., and Aziz, E. 2015. A modified ABC to Optimize the Parameters of Support Vector Machine for Predicting Bankruptcy. Proceedings of the World Congress on Engineering Vol. 1, London, U.K. ISBN: 978-988-19253-4-3
  7. Yigitbasi, E. D., and Baykan, N. A. 2013. Edge Detection using Artificial Bee Colony Algorithm (ABC). International Journal of Information and Electronics Engineering, Vol. 3, No. 6, November Edition.
  8. Kiran, M. S. and Gunduz, M. 2013. XOR-based Artificial Bee Colony Algorithm for binary optimization. Turkish Journal of Electrical Engineering and Computer Sciences, Vol. 21: 2307 -2328, Tubitak.
  9. Osman, H., Omar, S. S., and Mustafa, A. S. 2014. LSSVM ABC Algorithm for Stock Price prediction. International Journal of Computer Trends and Technology (IJCTT) – volume 7, number 2, Page 81, – Jan 2014, ISSN: 2231-2803.
  10. Nadezda, S., Milan, T., and Nebojsa, B. 2010. Enhanced Artificial Bee Colony Algorithm Performance. Latest Trends on Computers (Volume II), Faculty of Computer Science, Megatrend University Belgrade, Serbia
  11. Baykasoglu, A., Ozbakır, L., and Tapkan, P. 2007. Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem, Swarm Intelligence: Focus on Ant and Particle Swarm Optimization. I-Tech Education and Publishing.
  12. Zhu, G., and Kwong, S. 2010. Gbest-guided Artificial Bee Colony Algorithm for numerical function algorithm. Applied Mathematics and Computation, Vol. 217, pp. 3166-3173 @ Elsevier Inc.
  13. Alatas, B. 2010. Chaotic bee colony algorithms for global numerical optimization. Expert Systems with Applications, Vol. 37, pp. 5682 5687.
  14. Mansouri, P., Asady, B., and Gupta, N. 2014. Solve Shortest Paths Problem by Using Artificial Bee Colony Algorithm. Proceedings of the Third International Conference on Soft Computing for Problem Solving, Advances in Intelligent Systems and Computing 258, DOI: 10.1007/978-81-322-1771-8_16, Springer India.
  15. Yuster, R. 2012. Approximate shortest paths in weighted graphs. Journal of Computer and System Sciences, Vol. 78, pp. 632–637, Elsevier.
  16. Jagtap, A. M. and Gomathi, N. 2017. Minimizing sensor movement in target coverage problem: A hybrid approach using Voronoi partition and swarm intelligence. Bulletin of the polish academy of sciences technical sciences, Vol. 65, No. 2.
  17. Liisa, L., Juha-Matti, J., Sami, K., and Laura, P. 2017. The Constructive Research Approach: Problem Solving for Complex Projects. Chapter 8 of Design, Methods and practices for Research of Project Management by Beverly pasian.
  18. Karaboga, D. and Ozturk, C. 2011. A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Elsevier journal, Applied Soft Computing, Vol. 11, pp.652-657.
  19. Ajayan, A. R. and Balaji, S. 2013. A Modified ABC Algorithm and its application to wireless sensor network dynamic deployment. Journal of Electronics and Communication Engineering (IOSR-JECE), IOSR e-ISSN: 2278-2834, p- ISSN: 2278-8735. Volume 4, Issue 6.
  20. Ali, H., Sina, K. A. and Saeid K. A. 2010. Structural optimization using artificial bee colony algorithm. 2nd International Conference on Engineering Optimization September 6 - 9, Lisbon, Portugal.
  21. Karaboga, D. 2010. Artificial bee colony algorithm. Scholarpedia, 5(3):6915.
  22. Oshaba, A. S., Ali, .E. S., and Abd, E. S. M. 2015. Artificial Bee Colony Algorithm Based Maximum Power Point Tracking in Photovoltaic System. WSEAS transactions on power systems Volume 10: E-ISSN: 2224-350X.
Index Terms

Computer Science
Information Sciences

Keywords

Artificial bee colony fire outbreaks swarm-based heuristic fault resistance colony size and max cycles.

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