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Estimation of parameters of non-linear regression based on PSOGSA algorithm | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 5، دوره 14، شماره 6، شهریور 2023، صفحه 59-71 اصل مقاله (637.49 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.6981 | ||
| نویسندگان | ||
| Ibetehaj Loqman؛ Iraq T. Abass* | ||
| Department of Mathematics, University of Baghdad, Baghdad 00964, Iraq | ||
| تاریخ دریافت: 13 فروردین 1401، تاریخ بازنگری: 28 خرداد 1401، تاریخ پذیرش: 21 مرداد 1401 | ||
| چکیده | ||
| Although computational strategies for taking care of Non-linear Regression Based on Hybrid Algorithms (EPNRHA) to Estimation of Parameters have always been available for years, the further application of Evolutionary Algorithms (EAs) to such difficulties provides a framework for addressing a wide range of Multi-Objective Conflicts (MOPs). NRPSOGSA is an Estimation of Parameters of Non-linear Regression Gravitational Search Algorithm with Practical Swarm Optimization that involves the synthesis of hegemony by using the hybrid algorithm (PSOGSA) approach is utilized. Whilst Gravitational Search Algorithm with Practical Swarm Optimization Since the leader hiring process uses the Tchebycheff Strategy as a criterion, simplifying the multi-objective problem (MOP) by rewriting it as a set of Tchebycheff Approach, solving these issues at the same time within the GSA context may lead to rapid resolution. Dominance is important in constructing the leader's library because it allows the chosen leaders to encompass fewer dense places, reducing global optimization problems and producing a more diverse approximated Pareto front. 6 non-linear standard functions yielded this result. PSOGSA appears to be more productive than GSA, PSO, and BAT. All of the outcomes were completed. by MATLAB (R2020b). | ||
| کلیدواژهها | ||
| Estimation Parameter؛ Practice Swarm Algorithm؛ Non-Linear Regression | ||
| مراجع | ||
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[1] H.A. Alsattar, A.A. Zaidan and B.B. Zaidan, Novel meta-heuristic bald eagle search optimization algorithm, Artific. Intel. Rev. 53 (2020), no. 3, 2237–2264. [2] H.A. AlSattar, A.A. Zaidan, B.B. Zaidan, M.A. Bakar, R.T. Mohammed, O.S. Albahri, M.A. Alsalem and A.S. Albahri, MOGSABAT: a metaheuristic hybrid algorithm for solving multi-objective optimisation problems, Neural Comput. Appl. 32 (2020), no. 8, 3101–3115. [3] W.M. Aly, Evaluation of Cuckoo search usage for model parameters estimation, Int. J. Comput. Appl. 78 (2013), no. 1, 1–6. [4] S.A. Angayarkanni, R. Sivakumar and Y.R. Rao, Hybrid grey wolf: Bald eagle search optimized support vector regression for traffic flow forecasting, J. Ambient Intel. Human. Comput. 12 (2021), no. 1, 1293–1304. [5] P.A.N. Bosman and D. Thierens, The balance between proximity and diversity in multi-objective evolutionary algorithms, IEEE Trans. Evol. Comput. 7 (2003), no. 2, 174–188. [6] H. Bunke, 18 Parameter estimation in nonlinear regression models, Elsevier, Handbook of Statistics 1 (1980), pp. 593-615. [7] C. Coello Coello and M. Salazar Lechuga, MOPSO: A proposal for multiple objective particle swarm optimization, Cong. Evol. Comput. (CEC’2002), 2002, pp. 1051–1056. [8] D. Cuthbert, F.S. Wood and J.W. Gorman, Fitting equation to data: Computer analysis of multifactor data, Wiley, New York, 1980. [9] R. Eberhart and J. Kennedy, Particle swarm optimization, Proc. IEEE Int. Conf. Neural Networks, 1995, November, pp. 1942–1948. [10] F. Galton, Arithmetic by smell, Psych. Rev. 1 (1894), 61–62. [11] D.M. Gujarati, Basic Econometrics,4th ed., Tata McGraw Hill, New Delhi, Holland, J. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press, Michigan, 2004. [12] H.H. Huang, C.K. Hsiao, S.Y. Huang, P. Peterson, E. Baker and B. McGaw, Nonlinear regression analysis, Int. Encycl. Educ., 2010, pp. 339–346. [13] M. Kapanoglu, I. Ozan Koc and S. Erdogmus, Genetic algorithms in parameter estimation for nonlinear regression models: an experimental approach, J. Statist. Comput. Simul. 77 (2007), no. 10, 851–867. [14] E. Rashedi, H. Nezamabadi-Pour and S. Saryazdi, GSA:agravitational search algorithm, Inf. Sci. 179 (2009), no. 13, 2232–2248. [15] R.H. Sheah and I.T. Abbas, Using multi-objective bat algorithm for solving multi-objective mon-linear programming problem, Iraqi J. Sci. 62 (2021), no. 3, 997–1015. [16] X.S. Yang and S. Deb, Engineering optimization by Cuckoo search, Int. J. Math. Model. Numer. Optim. 1 (2010), no. 4, 330–343. [17] X.S. Yang and A.H. Gandomi, Bat algorithm: a novel approach for global engineering optimization, Engin. Comput. 29 (2012), no. 5, 1–18. | ||
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