Call for Paper - September 2020 Edition
IJCA solicits original research papers for the September 2020 Edition. Last date of manuscript submission is August 20, 2020. Read More

The Use of Original and Hybrid Grey Wolf Optimizer in Estimating the Parameters of Software Reliability Growth Models

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Jamal Salahaldeen Majeed Alneamy, Marwah Marwan Abdulazeez Dabdoob
10.5120/ijca2017914201

Jamal Salahaldeen Majeed Alneamy and Marwah Marwan Abdulazeez Dabdoob. The Use of Original and Hybrid Grey Wolf Optimizer in Estimating the Parameters of Software Reliability Growth Models. International Journal of Computer Applications 167(3):12-21, June 2017. BibTeX

@article{10.5120/ijca2017914201,
	author = {Jamal Salahaldeen Majeed Alneamy and Marwah Marwan Abdulazeez Dabdoob},
	title = {The Use of Original and Hybrid Grey Wolf Optimizer in Estimating the Parameters of Software Reliability Growth Models},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2017},
	volume = {167},
	number = {3},
	month = {Jun},
	year = {2017},
	issn = {0975-8887},
	pages = {12-21},
	numpages = {10},
	url = {http://www.ijcaonline.org/archives/volume167/number3/27750-2017914201},
	doi = {10.5120/ijca2017914201},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

In order to optimize the use of programs, it has become necessary to focus on issues like software reliability. In this work, the parameters of Software Reliability Growth Models (SRGMs) were estimated in depending on failure data and Swarm Intelligence, namely, Grey Wolf Optimizer (GWO). Then, the (GWO) was hybrid with Real Coded Genetic Algorithm (RGA) to obtain Hybrid GWO (HGWO).

The results that obtained from (GWO) are compared to the results of five algorithms: Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), the Dichotomous Artificial Bee Colony (DABC), Classic Genetic Algorithm (CGA) and the Modified Genetic Algorithm (MGA).

The results showed that (GWO) outperformed the rest of the algorithms in parameters estimating accuracy and performance using identical datasets. Sometimes, the (DABC) showed better performance than (GWO).

Other comparisons were made between (GWO) and (HGWO) and the results show that the hybrid algorithm outperformed the original one.

References

  1. Sheakh, T. H., Singh, V. P., 2012,"Taxonomical Study Of Software Reliability Growth Models", International Journal of Scientific and Research Publications, Volume 2, Issue 5, pp.1-3.
  2. Kaswan, K.S., Choudhary , S., Sharma, K., 2015," Software Reliability Modeling using Soft Computing Techniques: Critical review", J Inform Tech Softw Eng 5: 144.
  3. Xie, M., Dai, Y. S., Poh, K. L., 2004, "Computing System Reliability Models and Analysis", Springer, ISBN-10: 030648496X, ISBN-13:978-0306484964, pp.1-293.
  4. Wood, A., 1996, "Software Reliability Growth Models", Tandem Tech., Technical Report, Vol. 96.1, Tandem Computers Inc., Corporate Information Center, Cupertino Calif., Part Number 130056.
  5. Hsu, C.J., Huang, C.Y., 2010," A Study on the Applicability of Modified Genetic Algorithms for the Parameter Estimation of Software Reliability Modeling " , IEEE 34th Annual Computer Software and Applications Conference, pp.531-540.
  6. Shanmugam, L., Florence, L., 2012, "A Comparison of Parameter Best Estimation Method for Software Reliability Models", International Journal of Software Engineering & Applications (IJSEA), Vol.3, No.5, pp.91-102.
  7. Su, Y.S., Huang, C.Y., 2006," Neural-network-based approaches for software reliability estimation using dynamic weighted combinational models ", The Journal of Systems and Software 80, pp.606–615.
  8. AL-Saati, N., Abd-AlKareem, M.,2013," The Use of Cuckoo Search in Estimating the Parameters of Software Reliability Growth Models", International Journal of Computer Science and Information Security, Vol. 11, No. 6.
  9. Kelanibandara, K.W.K.B.P.L.M.,2012," Software Reliability Estimation Using Cubic Splines Network Model", thesis, University of Colombo School of Computing , pp.1-72.
  10. Lai, R., Garg, M., 2012, "A Detailed Study of NHPP Software Reliability Models", Journal of Software, Vol.7, No.6, pp.1296-1306.
  11. Wohlin, C., Höst, M., Runeson, P., Wesslén, A., 2001, " Software Reliability", Encyclopedia of Physical Science and Technology, Volume 15, pp.1-27.
  12. Meyfroyt, P. H. A., 2012,"Parameter Estimation for Software Reliability Models", thesis, Eindhoven: Technische Universiteit Eindhoven, pp.1-65.
  13. Song, K. Y., Chang, I. H., 2014," Parameter Estimation and Prediction for NHPP Software Reliability Model and Time Series Regression in Software Failure Data", J. Chosun Natural Sci., Vol. 7, No. 1, pp. 67 – 73.
  14. Williams, P., 2006,"prediction capability analysis of two and three parameters software reliability growth models", information technology journal 5(6), pp.1048-1052.
  15. Ohba, M., 1984,"software reliability analysis models", IBM J. RES. DEVELOP. VOL. 28 NO. 4, pp.228-443.
  16. Sheta, A. F., 2007, " Parameter Estimation of Software Reliability Growth Models by Particle Swarm Optimization", AIML Journal, Volume (7), Issue (1), pp.55-61.
  17. Sharma, T.K., Pant, M., Abraham, A., 2011," Dichotomous Search in ABC and its Application in Parameter Estimation of Software Reliability Growth Models ", Third World Congress on Nature and Biologically Inspired Computing, pp.214-219.
  18. Wood A., 1996,"Predicting Software Reliability," IEEE Computer, vol. 29, no. 11, pp. 69-77.
  19. Jeske, D. R., Zhang, X., Pham, L., 2005," Adjusting Software Failure Rates That Are Estimated From Test Data ", IEEE TRANSACTIONS ON RELIABILITY, VOL. 54, NO. 1, pp.107–114.
  20. Mirjalili, S. A., Mirjalili , S. M., Lewis, A., 2014," Grey Wolf Optimizer", Advances in Engineering Software 69, pp. 46–61.
  21. Madadi, A., Motlagh, M. M., 2014," Optimal Control of DC motor using Grey Wolf Optimizer Algorithm", Tech J Engin & App Sci., 4 (4): 373-379.
  22. Mirjalili, S. A., 2015," How effective is the Grey Wolf optimizer in training multi-layer perceptrons",The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies 43:645.
  23. HERRERA, F., LOZANO, M., VERDEGAY, J.L., 1998," Tackling Real Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis", Artificial Intelligence Review 12: 265–319.
  24. Kumar, A., 2013," ENCODING SCHEMES IN GENETIC ALGORITHM", International Journal of Advanced Research in IT and Engineering, Vol.2 ,No.3, pp. 1-7.
  25. AL Neamy, J. S., 2006,"Brain Tumors Images Diagnosis Using Hybrid Intelligency Techniques", Ph.D. Thesis, college of computers and mathematics science/university of Mosul.
  26. Goldberg, D. E., Deb, K., 1991, "A Comparative Analysis of Selection Schemes Used in Genetic Algorithms", pp.70-92.
  27. Achiche, S., Baron, L., Balazinski, M., 2004," Real/binary-like coded versus binary coded genetic algorithms to automatically generate fuzzy knowledge bases: a comparative study", S. Achiche et al. / Engineering Applications of Artificial Intelligence 17, pp.313–325.
  28. Peltokangas, R., Sorsa, A., 2008," Real-coded genetic algorithms and nonlinear parameter identification", Report A No 34, pp.1-28.
  29. KAYA, Y., UYAR, M., TEKDN, R., 2011, "A Novel Crossover Operator for Genetic Algorithms: Ring Crossover".
  30. Michalewicz,Z.,1996,"Genetic Algorithms Data Structures =Evolution Programs", springer-verlag Berlin Heidelberg New York.

Keywords

Genetic algorithms, Grey Wolf optimizer, Software Reliability Growth Models .