Research article Special Issues

Jaya algorithm in estimation of P[X > Y] for two parameter Weibull distribution

  • Received: 15 September 2021 Accepted: 04 November 2021 Published: 19 November 2021
  • MSC : 62F10, 37N40

  • Jaya algorithm is a highly effective recent metaheuristic technique. This article presents a simple, precise, and faster method to estimate stress strength reliability for a two-parameter, Weibull distribution with common scale parameters but different shape parameters. The three most widely used estimation methods, namely the maximum likelihood estimation, least squares, and weighted least squares have been used, and their comparative analysis in estimating reliability has been presented. The simulation studies are carried out with different parameters and sample sizes to validate the proposed methodology. The technique is also applied to real-life data to demonstrate its implementation. The results show that the proposed methodology's reliability estimates are close to the actual values and proceeds closer as the sample size increases for all estimation methods. Jaya algorithm with maximum likelihood estimation outperforms the other methods regarding the bias and mean squared error.

    Citation: Saurabh L. Raikar, Dr. Rajesh S. Prabhu Gaonkar. Jaya algorithm in estimation of P[X > Y] for two parameter Weibull distribution[J]. AIMS Mathematics, 2022, 7(2): 2820-2839. doi: 10.3934/math.2022156

    Related Papers:

  • Jaya algorithm is a highly effective recent metaheuristic technique. This article presents a simple, precise, and faster method to estimate stress strength reliability for a two-parameter, Weibull distribution with common scale parameters but different shape parameters. The three most widely used estimation methods, namely the maximum likelihood estimation, least squares, and weighted least squares have been used, and their comparative analysis in estimating reliability has been presented. The simulation studies are carried out with different parameters and sample sizes to validate the proposed methodology. The technique is also applied to real-life data to demonstrate its implementation. The results show that the proposed methodology's reliability estimates are close to the actual values and proceeds closer as the sample size increases for all estimation methods. Jaya algorithm with maximum likelihood estimation outperforms the other methods regarding the bias and mean squared error.



    加载中


    [1] M. A. H. Sabry, E. M. Almetwally, O. A. Alamri, M. Yusuf, H. M. Almongy, A. S. Eldeeb, Inference of fuzzy reliability model for inverse rayleigh distribution, AIMS Math., 6 (2021), 9770-9785. doi: 10.3934/math.2021568. doi: 10.3934/math.2021568
    [2] X. Liu, L. Liu, Q. Wu, X. Yuan, H. Huang, Reliability analysis and evaluation of automotive seat angle-adjuster, Aust. J. Mech. Eng., 18 (2020), 481-489. doi: 10.1080/14484846.2018.1548720. doi: 10.1080/14484846.2018.1548720
    [3] S. A. Miller, A. Freivalds, A stress-strength interference model for predicting CTD probabilities, Int. J. Ind. Ergon., 15 (1995), 447-457. doi: 10.1016/0169-8141(94)00063-9. doi: 10.1016/0169-8141(94)00063-9
    [4] A. Kumar, M. Ram, System reliability analysis based on Weibull distribution and Hesitant fuzzy set, Int. J. Math. Eng. Manag. Sci., 3 (2018), 513-521. doi: 10.33889/IJMEMS.2018.3.4-037. doi: 10.33889/IJMEMS.2018.3.4-037
    [5] Q. Ramzan, M. Amin, A. Elhassanein, M. Ikram, The extended generalized inverted kumaraswamy weibull distribution: Properties and applications, AIMS Math., 6 (2021), 9955-9980. doi: 10.3934/math.2021579. doi: 10.3934/math.2021579
    [6] E. Ramos, P. L. Ramos, F. Louzada, Posterior properties of the Weibull distribution for censored data, Stat. Probabil. Lett., 166 (2020), 108873. doi: 10.1016/j.spl.2020.108873. doi: 10.1016/j.spl.2020.108873
    [7] R. Valiollahi, A. Asgharzadeh, M. Z. Raqab, Estimation of P (Y < X) for weibull distribution under progressive type-Ⅱ censoring, Commun. Stat.-Theory Methods., 42 (2013), 4476-4498. doi: 10.1080/03610926.2011.650265. doi: 10.1080/03610926.2011.650265
    [8] F. Louzada, P. L. Ramos, G. S. C. Perdoná, Different estimation procedures for the parameters of the extended exponential geometric distribution for medical data, Comput. Math. Methods Med., 2016 (2016), 8727951. doi: 10.1155/2016/8727951. doi: 10.1155/2016/8727951
    [9] K. C. Datsiou, M. Overend, Weibull parameter estimation and goodness-of-fit for glass strength data, Struct. Saf., 73 (2018) 29-41. doi: 10.1016/j.strusafe.2018.02.002. doi: 10.1016/j.strusafe.2018.02.002
    [10] F. Louzada, L. F. A. Alegria, D. Colombo, D. E. A. Martins, H. F. L. Santos, J. A. Cuminato, et al., A repairable system subjected to hierarchical competing risks: Modeling and applications, IEEE Access, 7 (2019), 171707-171723. doi: 10.1109/ACCESS.2019.2954767. doi: 10.1109/ACCESS.2019.2954767
    [11] F. Louzada, J. A. Cuminato, O. M. H. Rodriguez, V. L. D. Tomazella, P. H. Ferreira, P. L. Ramos, et al., Improved objective Bayesian estimator for a PLP model hierarchically represented subject to competing risks under minimal repair regime, PLoS One, 16 (2021), 1-25. doi: 10.1371/journal.pone.0255944. doi: 10.1371/journal.pone.0255944
    [12] M. P. Almeida, R. S. Paixão, P. L. Ramos, V. Tomazella, F. Louzada, R. S. Ehlers, Bayesian non-parametric frailty model for dependent competing risks in a repairable systems framework, Reliab. Eng. Syst. Safe., 204 (2020), 107145. doi: 10.1016/j.ress.2020.107145. doi: 10.1016/j.ress.2020.107145
    [13] M. Chacko, R. Mohan, Estimation of parameters of Kumaraswamy-Exponential distribution under progressive type-Ⅱ censoring, J. Stat. Comput. Simul., 87 (2017), 1951-1963. doi: 10.1080/00949655.2017.1300662. doi: 10.1080/00949655.2017.1300662
    [14] G. Tzavelas, Maximum likelihood parameter estimation in the three-parameter gamma distribution with the use of Mathematica, J. Stat. Comput. Sim., 79 (2009), 1457-1466. doi: 10.1080/00949650802403663. doi: 10.1080/00949650802403663
    [15] R. Aggarwala, N. Balakrishnan, Maximum likelihood estimation of the Laplace parameters based on progressive type-Ⅱ censored samples, In: Advances on methodological and applied aspects of probability and statistics, CRC Press, 2002.
    [16] H. K. T. Ng, L. Luo, Y. Hu, F. Duan, Parameter estimation of three-parameter Weibull distribution based on progressively Type-Ⅱ censored samples, J. Stat. Comput. Sim., 82 (2012), 1661-1678. doi: 10.1080/00949655.2011.591797. doi: 10.1080/00949655.2011.591797
    [17] T. A. Abushal, Parametric inference of akash distribution for type-ii censoring with analyzing of relief times of patients, AIMS Math., 6 (2021), 12911-12912. doi: 10.3934/math.2021627. doi: 10.3934/math.2021627
    [18] J. J. Swain, S. Venkatraman, J. R. Wilson, Least-squares estimation of distribution functions in Johnson's translation system, J. Stat. Comput. Sim., 29 (1988), 271-297. doi: 10.1080/00949658808811068. doi: 10.1080/00949658808811068
    [19] S. K. Ashour, M. A. Eltehiwy, Exponentiated power Lindley distribution, J. Adv. Res., 6 (2015) 895-905. doi: 10.1016/j.jare.2014.08.005. doi: 10.1016/j.jare.2014.08.005
    [20] C. B. Read, Weighted least squares, In: Encyclopedia of statistical sciences, Wiley, 2006,179-196. doi: 10.1002/0471667196.ess2909.pub2.
    [21] W. T. Wu, Y. T. Chu, K. C. Chen, Moving identification via weighted least-squares estimation, Int. J. Syst. Sci., 18 (1987), 477-486. doi: 10.1080/00207728708963981. doi: 10.1080/00207728708963981
    [22] S. G. From, A weighted least-squares procedure for estimating the parameters of Altham's multiplicative generalization of the binomial distribution, Stat. Probabil. Lett., 25 (1995), 193-199. doi: 10.1016/0167-7152(94)00222-t. doi: 10.1016/0167-7152(94)00222-t
    [23] S. Benchiha, A. I. Al-Omari, N. Alotaibi, M. Shrahili, Weighted generalized quasi lindley distribution: Different methods of estimation, applications for covid-19 and engineering data, AIMS Math., 6 (2021), 11850-11878. doi: 10.3934/math.2021688. doi: 10.3934/math.2021688
    [24] A. M. Almarashi, A. Algarni, M. Nassar, On estimation procedures of stress-strength reliability for Weibull distribution with application, PLoS One, 15 (2020), 1-23. doi: 10.1371/journal.pone.0237997. doi: 10.1371/journal.pone.0237997
    [25] W. S. Abu El Azm, E. M. Almetwally, A. S. Alghamdi, H. M. Aljohani, A. H. Muse, O. E. Abo-Kasem, Stress-strength reliability for exponentiated inverted Weibull distribution with application on breaking of Jute fiber and Carbon fibers, Comput. Intel. Neurosc., 2021 (2021), 1-21. doi: 10.1155/2021/4227346. doi: 10.1155/2021/4227346
    [26] A. M. Hamad, B. B. Salman, Different estimation methods of the stress-strength reliability restricted exponentiated Lomax distribution, Math. Model. Eng. Probl., 8 (2021), 477-484. doi: 10.18280/mmep.080319. doi: 10.18280/mmep.080319
    [27] R. M. Alotaibi, Y. M. Tripathi, S. Dey, H. R. Rezk, Bayesian and non-Bayesian reliability estimation of multicomponent stress-strength model for unit Weibull distribution, J. Taibah Univ. Sci., 14 (2020), 1164-1181. doi: 10.1080/16583655.2020.1806525. doi: 10.1080/16583655.2020.1806525
    [28] I. Pobočíková, Z. Sedliačková, Comparison of four methods for estimating the Weibull distribution parameters, Appl. Math. Sci., 8 (2014), 4137-4149. doi: 10.12988/ams.2014.45389. doi: 10.12988/ams.2014.45389
    [29] S. Pant, A. Kumar, S. Bhan, M. Ram, A modified particle swarm optimization algorithm for nonlinear optimization, Nonlinear Stud., 24 (2017), 127-138.
    [30] L. Sahoo, A. K. Bhunia, D. Roy, Reliability optimization in stochastic domain via genetic algorithm, Int. J. Qual. Reliab. Manage., 31 (2014), 698-717. doi: 10.1108/IJQRM-06-2011-0090. doi: 10.1108/IJQRM-06-2011-0090
    [31] R. V. Rao, Jaya: An advanced optimization algorithm and its engineering applications, Springer International Publishing, 2019. doi: 10.1007/978-3-319-78922-4.
    [32] D. B. Meshram, Y. M. Puri, N. K. Sahu, Multi-objective optimization for improving performance characteristics of novel curved EDM process using Jaya algorithm, In: Nature-inspired optimization in advanced manufacturing processes and systems, CRC Press, 2020.
    [33] R. H. Caldeira, A. Gnanavelbabu, Solving the flexible job shop scheduling problem using an improved Jaya algorithm, Comput. Ind. Eng., 137 (2019), 106064. doi: 10.1016/j.cie.2019.106064. doi: 10.1016/j.cie.2019.106064
    [34] S. Gupta, I. Agarwal, R. S. Singh, Workflow scheduling using Jaya algorithm in cloud, Concurr. Comput., 31 (2019), 1-13. doi: 10.1002/cpe.5251. doi: 10.1002/cpe.5251
    [35] R. Jin, L. Wang, C. Huang, S. Jiang, Wind turbine generation performance monitoring with Jaya algorithm, Int. J. Energy Res., 43 (2019), 1604-1611. doi: 10.1002/er.4382. doi: 10.1002/er.4382
    [36] D. C. Du, H. H. Vinh, V. D. Trung, N. T. Hong Quyen, N. T. Trung, Efficiency of Jaya algorithm for solving the optimization-based structural damage identification problem based on a hybrid objective function, Eng. Optim., 50 (2018), 1233-1251. doi: 10.1080/0305215X.2017.1367392. doi: 10.1080/0305215X.2017.1367392
    [37] D. Ezzat, S. Amin, H. A. Shedeed, M. F. Tolba, Directed jaya algorithm for delivering nano-robots to cancer area, Comput. Methods Biomech. Biomed. Eng., 23 (2020), 1306-1316. doi: 10.1080/10255842.2020.1797698. doi: 10.1080/10255842.2020.1797698
    [38] W. H. El-Ashmawi, A. F. Ali, A. Slowik, An improved Jaya algorithm with a modified swap operator for solving team formation problem, Soft Comput., 24 (2020), 16627-16641. doi: 10.1007/s00500-020-04965-x. doi: 10.1007/s00500-020-04965-x
    [39] R. V. Rao, D. P. Rai, Optimization of submerged arc welding process parameters using quasi-oppositional based Jaya algorithm, J. Mech. Sci. Technol., 31 (2017), 2513-2522. doi: 10.1007/s12206-017-0449-x. doi: 10.1007/s12206-017-0449-x
    [40] S. P. Singh, T. Prakash, V. P. Singh, M. G. Babu, Analytic hierarchy process based automatic generation control of multi-area interconnected power system using Jaya algorithm, Eng. Appl. Artif. Intell., 60 (2017), 35-44. doi: 10.1016/j.engappai.2017.01.008. doi: 10.1016/j.engappai.2017.01.008
    [41] M. G. Badar, A. M. Priest, Statistical aspects of fibre and bundle strength in hybrid composites, In: T. Hayashi, K. Kawata, S. Umekawa, Progress in science and engineering composites, Tokyo: ICCM-IV, 1982, 1129-1136.
    [42] H. H. Örkcü, E. Aksoy, M. I. Dogan, Estimating the parameters of 3-p Weibull distribution through differential evolution, Appl. Math. Comput., 251 (2015), 211-224. doi: 10.1016/j.amc.2014.10.127. doi: 10.1016/j.amc.2014.10.127
    [43] H. H. Örkcü, V. S. Özsoy, E. Aksoy, M. I. Dogan, Estimating the parameters of 3-p Weibull distribution using particle swarm optimization: A comprehensive experimental comparison, Appl. Math. Comput., 268 (2015), 201-226. doi: 10.1016/j.amc.2015.06.043. doi: 10.1016/j.amc.2015.06.043
    [44] B. Abbasi, A. H. Eshragh Jahromi, J. Arkat, M. Hosseinkouchack, Estimating the parameters of Weibull distribution using simulated annealing algorithm, Appl. Math. Comput., 183 (2006), 85-93. doi: 10.1016/j.amc.2006.05.063. doi: 10.1016/j.amc.2006.05.063
    [45] S. Acitas, C. H. Aladag, B. Senoglu, A new approach for estimating the parameters of Weibull distribution via particle swarm optimization: An application to the strengths of glass fibre data, Reliab. Eng. Syst. Saf., 183 (2019), 116-127. doi: 10.1016/j.ress.2018.07.024. doi: 10.1016/j.ress.2018.07.024
  • Reader Comments
  • © 2022 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1915) PDF downloads(108) Cited by(2)

Article outline

Figures and Tables

Figures(17)  /  Tables(7)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog